-
1Dissertation/ Thesis
المؤلفون: Gajić, Bojana
Thesis Advisors: Baldrich i Caselles, Ramon, Gatta, Carlo
المصدر: TDX (Tesis Doctorals en Xarxa)
مصطلحات موضوعية: Visió per computador, Visión por computador, Computer vision, Aprenentatge computacional, Aprendizaje computacional, Machine learning, Matemàtiques aplicades, Matemáticas aplicadas, Applied mathematics, Aprenentatge de mètriques, Aprendizaje de métricas, Metric learning, Recuperació d’instàncies, Recuperación de instancias, Instance retrieval, Re-identificac, Re-identificación, Re-identification, Tecnologies
وصف الملف: application/pdf
URL الوصول: http://hdl.handle.net/10803/673961
-
2Dissertation/ Thesis
المؤلفون: Rodríguez López, Pau
المساهمون: University/Department: Universitat Autònoma de Barcelona. Departament de Ciències de la Computació
Thesis Advisors: Gonzàlez i Sabaté, Jordi, Gomfaus Sitjes, Josep M., Roca Marvà, F. Xavier
المصدر: TDX (Tesis Doctorals en Xarxa)
مصطلحات موضوعية: Visió per computador, Visión por computador, Computer vision, Aprenentatge computacional, Aprendizaje computacional, Machine learning, Classificació d'imatges, Clasificación de imágenes, Image classification, Tecnologies
وصف الملف: application/pdf
URL الوصول: http://hdl.handle.net/10803/667196
-
3Academic Journal
المؤلفون: Rusiñol, Marçal
المصدر: Item: revista de biblioteconomia i documentació; 2019: Núm.: 65-66 (2018-2019)
مصطلحات موضوعية: Anàlisi de documents, visió per computador, processament del llenguatge natural, aprenentatge computacional, aprenentatge profund, Análisis de documentos, visión por computador, procesa-miento del lenguaje natural, aprendizaje computacional, aprendizaje profundo, Document analysis, computer vision, natural language pro-cessing, machine learning, deep learning
وصف الملف: application/pdf
-
4Conference
المؤلفون: Atencia-Ruiz, Miguel Alejandro
مصطلحات موضوعية: Redes neuronales (Informática), Sistemas autoorganizativos, Echo State Networks, Aprendizaje Computacional, Sistemas Dinámicos
Relation: Erasmus+ KA171 - Universidad de Holguín; Holguín, Cuba; 26/1/2023; https://hdl.handle.net/10630/30924
الاتاحة: https://hdl.handle.net/10630/30924
-
5Conference
المؤلفون: González Hernández, José, Guzmán Sánchez, José Luis, Acién, Gabriel, Ciardi, Martina, Moreno Úbeda, José Carlos
مصطلحات موضوعية: Aprendizaje computacional, Red convolucional, Microalgas, Machine learning, Convolutional networks, Microalgae
Relation: info:eu-repo/grantAgreement/EC/HE/101060991; https://doi.org/10.17979/spudc.9788497498609.399; González Hernández, J., Guzmán Sánchez, J.L., Acién Fernández, F.G., Ciardi, M., Moreno Ubeda, J.C. 2023. Uso de redes neuronal convolucionales 1D en espectrometrías para clasificación de géneros de microalgas. XLIV Jornadas de Automática, 399-404. https://doi.org/10.17979/spudc.9788497498609.399; http://hdl.handle.net/2183/33730
-
6Book
المؤلفون: José Joaquín Vargas Camacho, Rubén Felipe Morales Camargo, Cheryl Benítez Barajas, Yolanda Rojas Pulido, Edisson Leonardo Parra Herrera, Jorge Iván Rodríguez Peña, Óscar Emilio Alfonso Talero, Eliana Marcela Avendaño León, William Andrés Prieto Galindo, Sindy Paola Joya Cruz, Rossmajer Guataquira Lopez, Martha Cecilia Clavijo Riveros, Claudia Maria Arias Arias, Viviana Uni Muñoz, Angie carolina Cruz Caceres, Jairo Nelson Pulido
المساهمون: Instituto para la Investigación Educativa y el Desarrollo Pedagógico, IDEP, Secretaría de Educación de Bogotá
مصطلحات موضوعية: Animales de compañía, Competencias investigativas, Pandemia, Reflexión pedagógica, Matemàticas, Memoria histórica, Conflicto armado, Reparación simbólica, Narrativas, Ambiente de aprendizaje computacional, Investigación aplicada, Proyecto de investigación, Metodología, Premio, Reconocimiento
وصف الملف: application/pdf
Relation: Premio a la Investigación e Innovación Educativa; https://repositorio.idep.edu.co/handle/001/2569
-
7Academic Journal
المؤلفون: Roberto Emilio Salas ruiz, Jorge Enrique Rodriguez Rodriguez, Claudia Liliana Hernández García
المصدر: Revista Vínculos, Vol 17, Iss 2, Pp 97-103 (2020)
مصطلحات موضوعية: algoritmos híbridos, redes neuronales artificiales, recocido simulado, predicción de datos, aprendizaje computacional, Electronic computers. Computer science, QA75.5-76.95, Computer software, QA76.75-76.765
وصف الملف: electronic resource
-
8Academic Journal
المؤلفون: Niño-Rondón, Carlos Vicente, Castellano-Carvajal, Diego Andrés, Medina-Delgado, Byron, Castro-Casadiego, Sergio Alexander, Guerra-Ibarra, Dinael
المصدر: Eco Matemático; Vol. 13 Núm. 1 (2022): Enero - Junio; 34-45 ; 2462-8794 ; 1794-8231
مصطلحات موضوعية: Skin lesions, feature extraction, computational learning, open-source tools, Lesiones cutáneas, extracción de características, aprendizaje computacional, herramientas de código abierto
وصف الملف: application/pdf; text/html
Relation: https://revistas.ufps.edu.co/index.php/ecomatematico/article/view/3286/3977; https://revistas.ufps.edu.co/index.php/ecomatematico/article/view/3286/4282; E. R. Parker, “The influence of climate change on skin cancer incidence – A review of the evidence,” Int. J. Women’s Dermatology, vol. 7, no. 1, pp. 17–27, Jan. 2021, doi:10.1016/J.IJWD.2020.07.003.; C. Magalhaes, J. M. R. S. Tavares, J. Mendes, and R. Vardasca, “Comparison of machine learning strategies for infrared thermography of skin cancer,” Biomed. Signal Process. Control, vol. 69, pp. 1–10, Aug. 2021, doi:10.1016/j.bspc.2021.102872.; T. G. Chandra, A. M. T. Nasution, and I. C. Setiadi, “Melanoma and nevus classification based on asymmetry, border, color, and GLCM texture parameters using deep learning algorithm,” in AIP Conference Proceedings, Dec. 2019, vol. 2193, no. 1, pp. 1–6, doi:10.1063/1.5139389.; S. A. A. Ahmed, B. Yanikoglu, O. Goksu, and E. Aptoula, “Skin Lesion Classification with Deep CNN Ensembles,” 2020 28th Signal Process. Commun. Appl. Conf. SIU 2020 - Proc., Oct. 2020, doi:10.1109/SIU49456.2020.9302125.; E. Almansour and M. Arfan Jaffar, “Classification of Dermoscopic Skin Cancer Images Using Color and Hybrid Texture Features,” IJCSNS Int. J. Comput. Sci. Netw. Secur., vol. 16, no. 4, 2016.; S. Afifi, H. Gholamhosseini, and R. Sinha, “SVM classifier on chip for melanoma detection,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 270–274, Sep. 2017, doi:10.1109/EMBC.2017.8036814.; X. Li, J. Wu, H. Jiang, E. Z. Chen, X. Dong, and R. Rong, “Skin Lesion Classification Via Combining Deep Learning Features and Clinical Criteria Representations,” bioRxiv, pp. 1–7, Aug. 2018, doi:10.1101/382010.; K. Padmavathi and K. Thangadurai, “Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study,” Indian J. Sci. Technol., vol. 9, no. 6, pp. 1–6, Feb. 2016, doi:10.17485/IJST/2016/V9I6/77739.; M. Kumar, M. Alshehri, R. AlGhamdi, P. Sharma, and V. Deep, “A DE-ANN Inspired Skin Cancer Detection Approach Using Fuzzy C-Means Clustering,” Mob. Networks Appl. 2020 254, vol. 25, no. 4, pp. 1319–1329, Jun. 2020, doi:10.1007/S11036-020-01550-2.; A. M. Gajbar and A. . Deshpande, “GLCM and Multiclass Support Vector Machine Based Automatic Detection and Analysis of Types of Cancer and Skin Allergy,” Int. J. Adv. Res. Electron. Commun. Eng., vol. 4, no. 5, pp. 1477–1488, 2015.; J. Díaz Ríos, J. J. Payá Martínez, and M. E. Del Baño Aldedo, “El análisis textural mediante las matrices de co-ocurrencia (GLCM) sobre la imagen ecográfica del tendón rotuliano es de utilidad para la detección de cambios histológicos tras un entrenamiento con plataforma de vibración,” Cult. Cienc. y Deport., vol. 4, no. 11, pp. 91–102, 2009, Accessed: Jan. 20, 2022. [Online]. Available: https://dialnet.unirioja.es/servlet/articulo?codigo=3097046&info=resumen&idio ma=ENG.; J. Zhang, D. Mucs, U. Norinder, and F. Svensson, “LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity-Application to the Tox21 and Mutagenicity Data Sets,” J. Chem. Inf. Model., pp. 1–9, 2019, doi:10.1021/ACS.JCIM.9B00633/SUPPL_FILE/CI9B00633_SI_001.PDF.; C. Chen, Q. Zhang, Q. Ma, and B. Yu, “LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion,” Chemom. Intell. Lab. Syst., vol. 191, pp. 54–64, Aug. 2019, doi:10.1016/J.CHEMOLAB.2019.06.003.; M. Lingaraj, A. Senthilkumar, and J. Ramkumar, “Prediction of Melanoma Skin Cancer Using Veritable Support Vector Machine,” Ann. Rom. Soc. Cell Biol., vol. 25, pp. 2623 – 2636, Apr. 2021, Accessed: Dec. 14, 2021. [Online]. Available: https://www.annalsofrscb.ro/index.php/journal/article/view/2800.; J. Cervantes, F. Garcia-Lamont, L. Rodríguez-Mazahua, and A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189–215, Sep. 2020, doi:10.1016/J.NEUCOM.2019.10.118.; P. Srinivasan and V. Srinivasna, “A Comprehensive Diagnostic Tool for Skin Cancer Using a Multifaceted Computer Vision Approach,” 7th Int. Conf. Soft Comput. Mach. Intell. ISCMI 2020, pp. 213–217, Nov. 2020, doi:10.1109/ISCMI51676.2020.9311557.; P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci. Data 2018 51, vol. 5, no. 1, pp. 1–9, Aug. 2018, doi:10.1038/sdata.2018.161.; https://revistas.ufps.edu.co/index.php/ecomatematico/article/view/3286
-
9Dissertation/ Thesis
المؤلفون: Pérez del Pulgar Mancebo, Carlos Jesús
Thesis Advisors: Ingeniería de Sistemas y Automática, Muñoz Martínez, Víctor Fernando, García Morales, Isabel
مصطلحات موضوعية: Robótica, Inteligencia computacional, Aprendizaje computacional, Tesis doctoral, Hática
URL الوصول: http://hdl.handle.net/10630/12106
-
10Dissertation/ Thesis
Thesis Advisors: Iñesta Quereda, José Manuel, Rizo Valero, David, Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
مصطلحات موضوعية: Análisis tonal, Reconocimiento de patrones, Aprendizaje computacional, Interacción hombre-máquina, Interfaces de usuario, Lenguajes y Sistemas Informáticos
URL الوصول: http://hdl.handle.net/10045/54848
-
11Academic Journal
المؤلفون: Holman S. Cabezas, Wilson J. Sarmiento
المصدر: TecnoLógicas, Vol 22, Pp 33-47 (2019)
مصطلحات موضوعية: reconocimiento de gestos, interacción hombre-máquina, procesamiento de señales, aprendizaje computacional, reconocimiento de patrones, Technology, Engineering (General). Civil engineering (General), TA1-2040
وصف الملف: electronic resource
-
12Book
المؤلفون: Morales Aguilar, David
المساهمون: Bolaños Castro, Sandro Javier
مصطلحات موضوعية: Inteligencia artificial, Aprendizaje computacional, Lenguaje natural, Asistente virtual, Intenciones, Frases de entrenamiento, Entidades, Contextos, Ingeniería de Sistemas - Tesis y disertaciones académicas, Inteligencia artificial - Bogotá (Colombia), Lenguaje natural (Informática) - Bogotá (Colombia), Asistente virtual - Bogotá (Colombia), Aprendizaje automático (Inteligencia artificial) - Bogotá (Colombia), Universidad Distrital Francisco José de Caldas (Bogotá). Facultad de Ingeniería, Artifical inteligence, Machine learning, Natural language, Chatbot, Intents, Training phrases, Entities, Contexts
وصف الملف: pdf; application/pdf
Relation: http://hdl.handle.net/11349/29752
الاتاحة: http://hdl.handle.net/11349/29752
-
13Dissertation/ Thesis
المؤلفون: Moreno Trujillo, John Freddy
المساهمون: Hoyos Gómez, Nancy Milena, Moreno Trujillo, John Freddy 0000-0002-2772-6931, Moreno Trujillo, John Freddy https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001028324, Moreno Trujillo, John Freddy https://scholar.google.com/citations?user=j7aRNrAAAAAJ&hl=es
مصطلحات موضوعية: 330 - Economía::332 - Economía financiera, 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, 000 - Ciencias de la computación, información y obras generales::006 - Métodos especiales de computación, DERIVADOS FINANCIEROS, OPCIONES (FINANZAS), TEORIA DEL APRENDIZAJE COMPUTACIONAL, APRENDIZAJE SUPERVISADO (APRENDIZAJE AUTOMATICO), ECUACIONES DIFERENCIALES NO LINEALES, REDES NEURALES (COMPUTADORES), Derivative securities, Options (Finance), Computational learning theory, Supervised learning (Machine learning), Differential equations, nonlinear, Neural networks (Computer science), Mercado ilíquido, Valoración de derivados, Ecuaciones diferenciales parciales no lineales, Representación de Feynman-Kac, Redes neuronales artificiales, Illiquid market, Derivative valuation, Nonlinear partial differential equations, Feynman-Kac representation, Artificial neural networks
وصف الملف: x, 121 páginas; application/pdf
Relation: Acharya, V. V. and Pedersen, L. H. (2005). Asset pricing with liquidity risk. Journal of financial Economics, 77(2):375–410.; Albrecher, H., Guillaume, F., and Schoutens, W. (2013). Implied liquidity: model sensitivity. Journal of Empirical Finance, 23:48–67.; Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5(1):31–56.; Amihud, Y. (2019). Illiquidity and stock returns: A revisit. Critical Finance Review, (8):203–221.; Amihud, Y. and Mendelson, H. (1986). Asset pricing and the bid-ask spread. Journal of Financial Economics, 17(2):223–249.; Arenas, A. J., González-Parra, G., and Caraballo, B. M. (2013). A nonstandard finite difference scheme for a nonlinear black–scholes equation. Mathematical and Computer Modelling, 57(7-8):1663–1670.; Avellaneda, M., Levy, A., and Parás, A. (1995). Pricing and hedging derivative securities in markets with uncertain volatilities. Applied Mathematical Finance, 2(2):73–88.; Bachouch, A., Huré, C., Langrené, N., and Pham, H. (2021). Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications. Methodology and Computing in Applied Probability, pages 1–36.; Back, K. (1993). Asymmetric information and options. The Review of Financial Studies, 6(3):435–472.; Beck, C., Becker, S., Grohs, P., Jaafari, N., and Jentzen, A. (2021). Solving the kolmogorov pde by means of deep learning. Journal of Scientific Computing, 88:1–28.; Beck, C., Hutzenthaler, M., Jentzen, A., and Kuckuck, B. (2020). An overview on deep learning-based approximation methods for partial differential equations. arXiv preprint arXiv:2012.12348.; Beck, C., Weinan, E., and Jentzen, A. (2019). Machine learning approximation algorithms for high-dimensional fully nonlinear partial differential equations and second-order backward stochastic differential equations. Journal of Nonlinear Science, 29(4):1563–1619.; Bergman, Y. Z. (1995). Option pricing with differential interest rates. The Review of Financial Studies, 8(2):475–500.; Berkman, H. and Koch, P. D. (2008). Noise trading and the price formation process. Journal of Empirical Finance, 15(2):232–250.; Bertsimas, D. and Lo, A. W. (1998). Optimal control of execution costs. Journal of financial markets, 1(1):1–50.; Bismut, J.-M. (1973). Conjugate convex functions in optimal stochastic control. Journal of Mathematical Analysis and Applications, 44(2):384–404.; Björk, T. (2009). Arbitrage theory in continuous time. Oxford university press.; Black, F. and Scholes, M. (1973a). The pricing of options and corporate liabilities. Journal of political Economy, 81(3):637–654.; Black, F. and Scholes, M. (1973b). The pricing of options and corporate liabilities. Journal of political economy, 81(3):637–654.; Bouchard, B. (2015). Lecture notes on BSDEs Main existence and stability results. PhD thesis, CEREMADE-CEntre de REcherches en MAthématiques de la DEcision.; Brunetti, C. and Caldarera, A. (2004). Asset prices and asset correlations in illiquid markets. Available at SSRN 625184.; Caldarera, A., Brunetti, C., et al. (2005). Asset prices and asset correlations in illiquid markets. In 2005 Meeting Papers, number 288. Society for Economic Dynamics.; Cetin, U., Jarrow, R., Protter, P., and Warachka, M. (2006). Pricing options in an extended black scholes economy with illiquidity: Theory and empirical evidence. The Review of Financial Studies, 19(2):493–529.; Cetin, U., Jarrow, R. A., and Protter, P. (2010). Liquidity risk and arbitrage pricing theory. Springer.; Cheridito, P., Soner, H. M., Touzi, N., and Victoir, N. (2007). Second-order backward stochastic differential equations and fully nonlinear parabolic pdes. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 60(7):1081–1110.; Chordia, T., Roll, R., and Subrahmanyam, A. (2000). Commonality in liquidity. Journal of Financial Economics, 56(1):3–28.; Chordia, T., Roll, R., and Subrahmanyam, A. (2001). Market liquidity and trading activity. The Journal of Finance, 56(2):501–530.; Company, R., Jódar, L., and Pintos, J.-R. (2010a). Numerical analysis and computing for option pricing models in illiquid markets. Mathematical and computer modelling, 52(7- 8):1066–1073.; Company, R., Jódar, L., Ponsoda, E., and Ballester, C. (2010b). Numerical analysis and simulation of option pricing problems modeling illiquid markets. Computers & Mathematics with Applications, 59(8):2964–2975.; Corcuera, J. M., Guillaume, F., Madan, D. B., and Schoutens, W. (2012). Implied liquidity: towards stochastic liquidity modelling and liquidity trading. International Journal of Portfolio Analysis and Management, 1(1):80–91.; Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4):303–314.; Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press.; El Karoui, N. and Mazliak, L. (1997). Backward stochastic differential equations, volume 364. CRC Press.; Esser, A. (2004). Pricing in (in) complete markets: Structural analysis and applications. Springer Science & Business Media.; Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1):34–105.; Feng, S.-P., Hung, M.-W., and Wang, Y.-H. (2014). Option pricing with stochastic liquidity risk: Theory and evidence. Journal of Financial Markets, 18:77–95.; Forsyth, P. A. and Vetzal, K. R. (2012). Numerical methods for nonlinear pdes in finance. In Handbook of Computational Finance, pages 503–528. Springer.; Frey, R. (1998). Perfect option hedging for a large trader. Finance and Stochastics, 2(2):115–141.; Frey, R. (2000). Market illiquidity as a source of model risk in dynamic hedging. Model Risk, pages 125–138.; Frey, R. and Patie, P. (2002). Risk management for derivatives in illiquid markets: A simulation study. Advances in finance and stochastics, pages 137–159.; Frey, R. and Stremme, A. (1997). Market volatility and feedback effects from dynamic hedging. Mathematical finance, 7(4):351–374.; Geiss, S. and Ylinen, J. (2021). Decoupling on the Wiener Space, related Besov Spaces, and applications to BSDEs, volume 272. American Mathematical Society.; Gennotte, G. and Leland, H. (1990). Market liquidity, hedging, and crashes. The American Economic Review, pages 999–1021.; Germain, M., Pham, H., and Warin, X. (2021). Neural networks-based algorithms for stochastic control and pdes in finance. arXiv preprint arXiv:2101.08068.; Glover, K. J., Duck, P. W., and Newton, D. P. (2010). On nonlinear models of markets with finite liquidity: some cautionary notes. SIAM Journal on Applied Mathematics, 70(8):3252–3271.; Gökay, S., Roch, A. F., and Soner, H. M. (2011). Liquidity models in continuous and discrete time. Springer.; Gregory, J. (2015). The xVA Challenge: counterparty credit risk, funding, collateral and capital. John Wiley & Sons.; Guéant, O. (2016). The Financial Mathematics of Market Liquidity: From optimal execution to market making, volume 33. CRC Press.; Guo, J. andWang, W. (2015). On the numerical solution of nonlinear option pricing equation in illiquid markets. Computers & Mathematics with Applications, 69(2):117–133.; Gurney, K. (2018). An introduction to neural networks. CRC press; Guyon, J. and Henry-Labordere, P. (2013). Nonlinear option pricing. CRC Press.; Han, J., Jentzen, A., and E, W. (2018). Solving high-dimensional partial differential equations using deep learning. Proceedings of the National Academy of Sciences, 115(34):8505– 8510.; Heider, P. (2010). Numerical methods for non-linear black–scholes equations. Applied Mathematical Finance, 17(1):59–81.; Henry-Labordere, P. (2012). Counterparty risk valuation: A marked branching diffusion approach. arXiv preprint arXiv:1203.2369.; Henry-Labordere, P., Tan, X., and Touzi, N. (2014). A numerical algorithm for a class of bsdes via the branching process. Stochastic Processes and their Applications, 124(2):1112–1140.; Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366.; Hornik, K., Stinchcombe, M., and White, H. (1990). Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural networks, 3(5):551–560.; Huberman, G. and Stanzl, W. (2004). Arbitrage-free price update and price-impact functions. Econometrica, 72(4):1247–1275.; Huré, C., Pham, H., and Warin, X. (2020). Deep backward schemes for high-dimensional nonlinear pdes. Mathematics of Computation, 89(324):1547–1579.; Jarrow, R. A. (1994). Derivative security markets, market manipulation, and option pricing theory. Journal of financial and quantitative analysis, 29(2):241–261.; Karatzas, I. and Shreve, S. (2014). Brownian motion and stochastic calculus, volume 113. springer.; Karatzas, I. and Shreve, S. E. (1998). Methods of mathematical finance, volume 39. Springer.; Ku, H. and Zhang, H. (2018). Option pricing for a large trader with price impact and liquidity costs. Journal of Mathematical Analysis and Applications, 459(1):32–52.; Kyle, A. S. (1985). Continuous auctions and insider trading. Econometrica: Journal of the Econometric Society, pages 1315–1335.; Lagaris, I. E., Likas, A., and Fotiadis, D. I. (1998). Artificial neural networks for solving ordinary and partial differential equations. IEEE transactions on neural networks, 9(5):987–1000.; Leippold, M. and Schärer, S. (2017). Discrete-time option pricing with stochastic liquidity. Journal of Banking & Finance, 75:1–16.; Leland, H. E. (1985). Option pricing and replication with transactions costs. The journal of finance, 40(5):1283–1301.; Li, Z., Zhang, W.-G., and Liu, Y.-J. (2018a). Analytical valuation for geometric asian options in illiquid markets. Physica A: Statistical Mechanics and its Applications, 507:175–191.; Li, Z., Zhang, W.-G., and Liu, Y.-J. (2018b). European quanto option pricing in presence of liquidity risk. The North American Journal of Economics and Finance, 45:230–244.; Liesenfeld, R. and Richard, J.-F. (2003). Univariate and multivariate stochastic volatility models: estimation and diagnostics. Journal of empirical finance, 10(4):505–531.; Liu, H. and Yong, J. (2005). Option pricing with an illiquid underlying asset market. Journal of Economic Dynamics and Control, 29(12):2125–2156.; Liu, W. (2006). A liquidity-augmented capital asset pricing model. Journal of financial Economics, 82(3):631–671.; Lo, A. W., Mamaysky, H., and Wang, J. (2004). Asset prices and trading volume under fixed transactions costs. Journal of Political Economy, 112(5):1054–1090.; Loeper, G. (2018). Option pricing with linear market impact and nonlinear black–scholes equations. The Annals of Applied Probability, 28(5):2664–2726.; Longstaff, F. A., Mithal, S., and Neis, E. (2005). Corporate yield spreads: Default risk or liquidity? new evidence from the credit default swap market. The journal of finance, 60(5):2213–2253.; Ludkovski, M. and Shen, Q. (2013). European option pricing with liquidity shocks. International Journal of Theoretical and Applied Finance, 16(07):1350043.; Ma, J., Morel, J.-M., and Yong, J. (1999). Forward-backward stochastic differential equations and their applications. Number 1702. Springer Science & Business Media.; Madan, C. and Cherny, A. (2010). Illiquid markets as a counterparty: An introduction to conic finance, robert h. Smith School Research Paper No. RHS, pages 06–115.; Mandelbrot, B. B. (1997). The variation of certain speculative prices. Springer.; McKean, H. P. (1975). Application of brownian motion to the equation of kolmogorov-petrovskii-piskunov. Communications on pure and applied mathematics, 28(3):323–331.; Merton, R. C. (1973). Theory of rational option pricing. The Bell Journal of economics and management science, pages 141–183.; Merton, R. C. and Samuelson, P. A. (1992). Continuous-time finance.; Merton, R. C. and Samuelson, P. A. (1990). Continuous-time finance.; Mönch, B. (2006). Strategic trading in illiquid markets, volume 553. Springer Science & Business Media.; Nielsen, M. A. (2015). Neural networks and deep learning, volume 25. Determination pressSan Francisco, CA, USA.; Oksendal, B. (2013). Stochastic differential equations: an introduction with applications. Springer Science & Business Media.; Pardoux, E. and Peng, S. (1990). Adapted solution of a backward stochastic differential equation. Systems & control letters, 14(1):55–61.; Pardoux, E. and Tang, S. (1999). Forward-backward stochastic differential equations and quasilinear parabolic pdes. Probability theory and related fields, 114:123–150.; Pástor, L. and Stambaugh, R. F. (2003). Liquidity risk and expected stock returns. Journal of Political Economy, 111(3):642–685.; Pham, H., Warin, X., and Germain, M. (2021). Neural networks-based backward scheme for fully nonlinear pdes. SN Partial Differential Equations and Applications, 2(1):16.; Raissi, M. (2024). Forward–backward stochastic neural networks: deep learning of highdimensional partial differential equations. In Peter Carr Gedenkschrift: Research Advances in Mathematical Finance, pages 637–655. World Scientific.; Raissi, M. and Karniadakis, G. E. (2018). Hidden physics models: Machine learning of nonlinear partial differential equations. Journal of Computational Physics, 357:125–141.; Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686–707.; Ronnie Sircar, K. and Papanicolaou, G. (1998). General black-scholes models accounting for increased market volatility from hedging strategies. Applied Mathematical Finance, 5(1):45–82.; Shreve, S. E. (2004). Stochastic calculus for finance II: Continuous-time models, volume 11. Springer Science & Business Media.; Skorokhod, A. V. (1964). Branching diffusion processes. Theory of Probability & Its Applications, 9(3):445–449.; Subramanian, A. and Jarrow, R. A. (2001). The liquidity discount. Mathematical Finance, 11(4):447–474.; Tadmor, E. (2012). A review of numerical methods for nonlinear partial differential equations. Bulletin of the American Mathematical Society, 49(4):507–554.; Taylor, S. J. (1994). Modeling stochastic volatility: A review and comparative study. Mathematical finance, 4(2):183–204.; Tripathi, A., Dixit, A., et al. (2019). Liquidity of financial markets: a review. Studies in Economics and Finance.; Trujillo, J. F. M. (2015). Modelos estocásticos en finanzas. U. Externado de Colombia.; Vayanos, D. (2001). Strategic trading in a dynamic noisy market. The Journal of Finance, 56(1):131–171.; Vayanos, D. (2004). Flight to quality, flight to liquidity, and the pricing of risk. Technical report, National bureau of economic research.; Venegas-Martínez, F. (2008). Riesgos financieros y económicos, productos derivados y decisiones económicas bajo incertidumbre, volume 1. Escuela Superior de Economía, Instituto Politécnico Nacional; Warin, X. (2018). Nesting monte carlo for high-dimensional non-linear pdes. Monte Carlo Methods and Applications, 24(4):225–247.; Watanabe, S. (1965). On the branching process for brownian particles with an absorbing boundary. Journal of Mathematics of Kyoto University, 4(2):385–398.; Weinan, E., Hutzenthaler, M., Jentzen, A., and Kruse, T. (2017). Linear scaling algorithms for solving high-dimensional nonlinear parabolic differential equations. SAM Research Report, 2017.; Wilmott, P., Hoggard, T., and Whalley, A. E. (1994). Hedging option portfolios in the presence of transaction costs. Advances in Futures and Options Research, 7.; Wilmott, P. and Schönbucher, P. J. (2000). The feedback effect of hedging in illiquid markets. SIAM Journal on Applied Mathematics, 61(1):232–272.; Zhang, Y., Ding, S., and Duygun, M. (2019). Derivatives pricing with liquidity risk. Journal of Futures Markets, 39(11):1471–1485.; https://repositorio.unal.edu.co/handle/unal/86877; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/
-
14Dissertation/ Thesis
المساهمون: Gonzalez Sabaté, Jordi
مصطلحات موضوعية: Intel·ligència Artificial, Aprenentatge Computacional, Predicció, Violència de gènere, Anà lisi de dades, Inteligencia Artificial, Aprendizaje Computacional, Predicción, Violencia de género, Análisis de datos, Artificial Intelligence, Computational Learning, Prediction, Gender-based Violence, Data Analysis
وصف الملف: application/pdf
Relation: https://ddd.uab.cat/record/290082; urn:oai:ddd.uab.cat:290082; urn:tfgcv:2486205
الاتاحة: https://ddd.uab.cat/record/290082
-
15Academic Journal
المصدر: RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa; Vol. 26, Núm. 1 (2020): Monográfico: Evaluación en la Educación Superior ; RELIEVE - E-Journal of Educational Research, Assessment and Evaluation; Vol. 26, Núm. 1 (2020): Monográfico: Evaluación en la Educación Superior ; 1134-4032
مصطلحات موضوعية: Educación, minería de datos educativa, Ambiente estudiantil, Aprendizaje computacional, Árboles de decisión, Asesoramiento, Selección de atributos, Student environment, Computer learning, Decision trees, Counseling, Feature selection
وصف الملف: application/pdf
Relation: https://ojs.uv.es/index.php/RELIEVE/article/view/16061/15640; https://ojs.uv.es/index.php/RELIEVE/article/view/16061/15641; Abarca R., A., & Sánchez V., M. A. (2005). La deserción estudiantil en la educación superior: el caso de la Universidad de Costa Rica. Revista Electrónica "Actualidades Investigativas en Educación", 5 , 1-22. https://bit.ly/35TVeLE Abu-Oda, G. S., & El-Halees, A. M. (2015). Data mining in higher education: university student dropout case study. International Journal of Data Mining y Knowledge Management Process (IJDKP), 5(1), 15-27. https://doi.org/10.5121/ijdkp.2015.5102 Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4. https://doi.org/10.1109/ACCESS.2016.2568756 Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting student’s final GPA using decision trees: a case study. International Journal of Information and Education Technology, 6(7), 528-533. https://doi.org/10.7763/IJIET.2016.V6.745 Alkhasawneh, R., & Hargraves, R. H. (2014). Developing a Hybrid Model to Predict Student First Year Retention in STEM Disciplines Using Machine Learning Techniques. Journal of STEM Education: Innovations and Research, 5 (3), 35-42. ERIC. https://bit.ly/2Rd04hi Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2017). Predicting Student Dropout in Higher Education. Machine Learning in Social Good Applications , 16-20. https://bit.ly/3aRtae6 Barbosa M. L. M., Serra da Cruz, S. M., & Zimbrão, G. (2014). The Impact of High Dropout Rates in a Large Public Brazilian University: A Quantitative Approach Using Educational Data Mining. 6th International Conference on Computer Supported Education (págs. 124-129). Barcelona, Spain: INSTICC. https://bit.ly/2ZsYFbD Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. USA: O´Really Media, Inc. Bishop, C. M. (2007). Pattern recognition and Machine Learning. Singapore: Springer. Cabrera, L., Bethencour, J. T., Álvarez P. P., & González A. M. (2006). El problema del abandono de los estudios universitarios. RELIEVE, 12 (2), 171-203. https://doi.org/10.7203/relieve.12.2.4226 Carvajal O. P., & Trejos C. Á. A. (2016). Revisión de estudios sobre deserción estudiantil en educación superior en Latinoamérica bajo la perspectiva de Pierre Bourdieu. Congresos CLABES. Quito, Ecuador: Escuela Politécnica Nacional. https://bit.ly/2UP9mlT Cha, G.-W., Kim, Y.-C., Moon, H. J., & Hong, W.-H. (2017). New approach for forecasting demolition waste generation using chisquared automatic interaction detection (CHAID) method. Journal of Cleaner Production, 168, 375-385. https://doi.org/10.1016/j.jclepro.2017.09.025 Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., & Hong, H. (2017). GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics, Natural Hazards and Risk, 8(2), 950-973. https://doi.org/10.1080/19475705.2017.1289250 Chiheb, F., Boumahdi, F., Bouarfa, H., & Boukraa, D. (2017). Predicting students’ performance using decision trees: Case of an Algerian University. 2017 International Conference on Mathematics and Information Technology (ICMIT). Adrar, Algeria: IEEE. https://doi.org/10.1109/MATHIT.2017.8259704 Dekker, G. W., Pechenizki, M., & Vleeshouwers, J. M. (2009). Predicting Students Drop Out: A Case Study. 2nd International Conference on Educational Data Mining (págs. 41-50). Cordoba, Spain: International Educational Data Mining Society. https://bit.ly/2ZlH1a3 Delen, D. (2011). Predicting Student Attrition with Data Mining Methods. Journal of College Student Retention: Research, Theory y Practice, 13 (1), 17-35. https://doi.org/10.2190/CS.13.1.b Del Pobil, A. P., Mira, J., & Ali, M. (1998). Tasks and Methods in Applied Artificial Intelligence. 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems. 1416. Castellón, España: Springer. Estrada-Danell, R. I., Zamarripa-Franco, R. A., Zúñiga-Garay, P. G., & Martínez-Trejo, I. (2016). Aportaciones desde la minería de datos al proceso de captación de matrícula de instituciones de educación superior particulares. Revista Electrónica Educare, 20(3), 1-21. https://doi.org/10.15359/ree.20-3.11 Fozdar, B. I., Kumar, L. S., & Kannan, S. (2006). A Survey of a Study on the Reasons Responsible for Student Dropout from the Bachelor of Science Programme at Indira Gandhi National Open University. International Review of Research in Open and Distance Learning, 7 (3), 1-15. https://doi.org/10.19173/irrodl.v7i3.291 Frank, E., Hall, Mark A., & Witten I. H. (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016. Freitas, A. A. (2002). Data Mining and Knowledge Discovery with Evolutionary Algorithms. The Netherlands: Springer-Verlag. https://doi.org/10.1007/978-3-662-04923-5 Guevara, C., Sanchez-Gordon, S., Arias-Flores, H., Varela-Aldás, J., Castillo-Salazar, D., Borja, M., . . . Yandún-Velasteguí, M. (2019). Detection of Student Behavior Profiles Applying Neural Networks and Decision Trees. 1026, págs. 591-597. Munich, Germany: Springer, Cham. https://doi.org/10.1007/978-3-030-27928-8_9 Gupta, B., Rawat, A., Jain, A., Arora, A., & Dhami, N. (2017). Analysis of Various Decision Tree Algorithms for Classification in Data Mining. International Journal of Computer Applications, 163(8), 15-19. https://doi.org/10.5120/ijca2017913660 Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. Amsterdam: Morgan Kaufmann. INEGI. (2018). Estadísticas a propósito del día mundial de la población (11 de julio). Ciudad de México: INEGI. https://bit.ly/2xbnZHd Jadhav, S. D., & Channe, H. P. (2016). Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845. https://doi.org/10.21275/v5i1.NOV153131 Kabra, R. R., & Bichkar, R. S. (2011). Performance Prediction of Engineering Students using Decision Trees. International Journal of Computer Applications, 36 (11), 9-12. https://bit.ly/2JdxckV Kotsiantis, S., Pierrakeas, C., & Pintelas, P. E. (2003). Preventing Student Dropout in Distance Learning Using Machine Learning Techniques. Knowledge-Based Intelligent Information and Engineering Systems, 7th International Conference (págs. 267-274). Oxford, UK.: Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_37 Kumar Y. S., Bharadwaj, B., & Pal, S. (2012). Mining Education Data to Predict Student's Retention: A comparative Study. International Journal of Computer Science and Information Security, 10 (2), 113-117. https://bit.ly/2JJw9t1 Lavado, P., & Gallegos, J. (2005). La dinámica de la deserción escolar en el Perú: un enfoque usando modelos de duración. Lima, Perú: Universidad del Pacífico. https://bit.ly/39PH3rJ Londoño A. L. F. (2013). Factores de riesgo presentes en la deserción estudiantil en la Corporación Universitaria Lasallista. Revista Virtual Universidad Católica del Norte (38), 183-194. https://bit.ly/1OnjEwM Longest, K. C. (2019). Using Stata for Quantitative Analysis. California, USA: SAGE Publications. M.P. van der Aalst, W. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes (Google eBook). London, UK: Springer-Verlag. https://doi.org/10.1007/978-3-642-19345-3 Márquez-Vera C., Romero M. C., & Ventura S. S. (2012). Predicción del Fracaso Escolar mediante Técnicas de Minería de Datos. Revista Iberoamericana de Tecnologías del Aprendizaje, 7 (3), 109-117. https://bit.ly/2zoZKmo Márquez-Vera, C., Cano, A., Romero, C., Mohammad N. A. Y., Fardoun, H. M., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33 (1), 107-125. https://doi.org/10.1111/exsy.12135 Mitchell, T. M. (1997). Machine Learning. Singapore: McGraw-Hill. Mitchell, T. M. (2000). Decision Tree Learning. Washington State University. https://bit.ly/2N1AI32 Morales C. J., & Parraga-Alava, J. (2018). How Predicting The Academic Success of Students of the ESPAM MFL?: A Preliminary Decision Trees Based Study. 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM). Cuenca, Ecuador: IEEE. https://doi.org/10.1109/ETCM.2018.8580296 Morillas, A. (2014). Muestreo en poblaciones finitas. Notas del curso. Málaga-España: Universidad de Málaga. https://bit.ly/2JLLA3K OECD. (2017). Skills Strategy Diagnostic Report: Mexico 2017, OECD Skills Studies. París: OECD Publishing. https://doi.org/10.1787/9789264287679-en OECD. (2019). Higher Education in Mexico: Labour Market Relevance and Outcomes, Higher. París: OECD Publishing. https://doi.org/10.1787/9789264309432-en Pal, S. (2012). Mining Educational Data Using Classification to Decrease Dropout Rate of Students. International Journal of Multidisciplinary Sciences and Engineering, 3 (5), 35-39. https://bit.ly/2xVhAjc Raju, D. y Schumacker, R. (2015). Exploring Student Characteristics of Retention that Lead to Graduation in Higher Education Using Data Mining Models. Journal of college student retention: Research, Theory y Practice, 16(5), 563-591. https://doi.org/10.2190/CS.16.4.e Rodríguez-Maya, N. E., Lara-Álvarez, C., May-Tzuc, O., & Suárez-Carranza, B. A. (2017). Modeling Students' Dropout in Mexican Universities. Research in Computing Science, 139 , 163-175. https://doi.org/10.13053/rcs-139-1-13 Ruíz C., L. (2009). Deserción en la educación superior recinto Las Minas. Período 2001-2007. Ciencia e Interculturalidad, 4 (2), 30-46. https://doi.org/10.5377/rci.v4i1.288 Sara, N. B., Halland, R., Igel, C., & Alstrup, S. (2015). High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (págs. 319-324). Bruges, Belgium: i6doc.com. https://bit.ly/2MFgzkp Secretaría de Educación Pública. (2019). Abandono escolar. Ciudad de México: SEP. Secretaría de Educación Pública. (2019). Principales cifras del sistema educativo nacional 2018-2019. Ciudad de México: Dirección General de Planeación, Programación y Estadística. https://bit.ly/2yCwivX Sharma, H., & Kumar, S. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097. https://doi.org/10.21275/v5i4.NOV162954 Sivakumar, S., Venkataraman, S., & Selvaraj, R. (2016). Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree. Indian Journal of Science and Technology, 9 (4), 1-5. https://doi.org/10.17485/ijst/2016/v9i4/87032 Universidad Tecnológica de Tabasco. (2019). Glosario de Términos. Villermosa, Tabasco: Universidad Tecnológica de Tabasco. https://bit.ly/2xZ60DK Ustebay, S., Turgut, Z., & Ali A. M. (2018). Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) (págs. 71-76). Ankara, Turkey: IEEE. https://doi.org/10.1109/IBIGDELFT.2018.8625318 Vélez, A., & López, J. D. F. (2004). Estrategias para vencer la deserción universitaria. Educación y Educadores (7). 177-203. https://bit.ly/39MgeEJ Valle G. R., Eslava G. G., Manzano P. A., & García M. M. (2014). Encuesta Internacional sobre el Abandono en la Educación Superior. Unión Europea. https://bit.ly/2p8k2Pk Vijayalakshmi, M., & Kumar, A. S. (2011). Efficiency of decision trees in predicting student's academic performance. Computer Science y Information Technology, 335-343. https://doi.org/10.5121/csit.2011.1230 Vries, W., León A. P., Romero M. J. F., & Hernández S. I. (2011). ¿Desertores o decepcionados? Distintas causas para abandonar los estudios universitarios. Revista de la Educación Superior, 40 (160), 29-49. https://bit.ly/1TzOzru Witten, I. H., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. San Francisco, CA: ELSEVIER. Witten, I. H., Frank, E., & Hall, M.A. (2011). Data mining: Practical machine learning tools and techniques (3a. ed.). Morgan Kaufmann Publishers, Burlington. https://doi.org/10.1016/B978-0-12-374856-0.00001-8 Yamao, E., Saavedra, L. C., Campos P. R., & Huancas H. V. D. (2018). Prediction of academic performance using data mining in first year students of peruvian university. CAMPUS, XXIII (26), 151-160. https://doi.org/10.24265/campus.2018.v23n26.05 Yang, S., Lu, O., Huang, A., Huang , J., Ogata, H., & Lin, A. (2017). Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis. Journal of Information Processing, 170-176. https://doi.org/10.2197/ipsjjip.26.170 Yukselturk, E., Ozekes, S., & Kılıç T. Y. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and e-Learning, 17 (1), 119-133. https://doi.org/10.2478/eurodl-2014-0008; https://ojs.uv.es/index.php/RELIEVE/article/view/16061
-
16Academic Journal
المصدر: RELIEVE – Electronic Journal of Educational Research and Evaluation; Vol. 26 No. 1 (2020): EVALUACIÓN EN LA EDUCACIÓN SUPERIOR ; RELIEVE - Revista Electrónica de Investigación y Evaluación Educativa; Vol. 26 Núm. 1 (2020): EVALUACIÓN EN LA EDUCACIÓN SUPERIOR ; 1134-4032
مصطلحات موضوعية: Student environment, Computer learning, Decision trees, Counseling, Feature selection, Ambiente estudiantil, Aprendizaje computacional, Árboles de decisión, Asesoramiento, Selección de atributos, 学生氛围、机器学习、决策树、顾问、属性选择
وصف الملف: application/pdf
-
17Academic Journal
المؤلفون: Salas ruiz, Roberto Emilio, Rodriguez Rodriguez, Jorge Enrique, Hernández García , Claudia Liliana
المصدر: Revista Vínculos; Vol. 17 No. 2 (2020); 97-103 ; Revista Vínculos; Vol. 17 Núm. 2 (2020); 97-103 ; Revista Vínculos; v. 17 n. 2 (2020); 97-103 ; 2322-939X ; 1794-211X
مصطلحات موضوعية: hybrid algorithms, artificial neural networks, simulated annealing, data prediction, machine learning, algoritmos híbridos, redes neuronales artificiales, recocido simulado, predicción de datos, aprendizaje computacional
وصف الملف: application/pdf
Relation: https://revistas.udistrital.edu.co/index.php/vinculos/article/view/17232/17559; C. Clifton, “Data Mining Course Overview”, 2006. [En línea] Disponible en http://www.cs.purdue.edu/homes/clifton/cs590d/Intro.ppt [2] M. Duarte, F. Pantrigo y C. Gallego. “MetaHeurísticas”. Madrid: Dykinson, 2007. [3] J. Han, y M. Kamber, “Data mining, Concepts and techniques”, San Francisco, CA: Morgan Kaufmann, 2006, pp. 61 – 65, 110 – 127. [4] S. Haykin. “Neural Networks A comprensive foundation”. USA, 1999. [5] R. Hernández, C. Fernández y P. Baptista, Metodología de la investigación. México: McGraw-Hill Interamericana, 2003. [6] M. Kantardzic, “Data Mining: concepts, models, methods, and algorithms”. United States, 2001. [7] B. Kröse, y P. Smagt, “An Introduction to Neural Network”. Holland, 1996. p. 15; https://revistas.udistrital.edu.co/index.php/vinculos/article/view/17232
-
18Academic Journal
المصدر: Revista de Sistemas, Cibernética e Informática, Vol 17, Iss 1, Pp 90-95 (2020)
مصطلحات موضوعية: agrupación, clasificación, minería de datos educativa, aprendizaje computacional, Electronic computers. Computer science, QA75.5-76.95
Relation: http://www.iiisci.org/journal/CV$/risci/pdfs/CB865XX20.pdf; https://doaj.org/toc/1690-8627; https://doaj.org/article/8573383ef293442f8ce74642e9f3b6b7
-
19Academic Journal
المؤلفون: A. B. Urbina-Nájera, R.G. Morales-Salgado
المصدر: Coloquio de Investigación Multidisciplinaria, 7(1), 2113-2119, (2019-10-01)
مصطلحات موضوعية: Abandono escolar, Aprendizaje computacional, Bajo desempeño escolar
Relation: https://zenodo.org/communities/cim; https://doi.org/10.5281/zenodo.4268442; https://doi.org/10.5281/zenodo.4268443; oai:zenodo.org:4268443
-
20Academic Journal
المؤلفون: G. V. Glukhov, L. V. Kapustina, I. A. Martynova.
المصدر: Dilemas contemporáneos: Educación, Política y Valores; Año VII, Edición Especial Noviembre 2019 ; Dilemas contemporáneos: Educación, Política y Valores; Year VII, Special Edition, November 2019 ; 2007-7890
مصطلحات موضوعية: computer learning, content and language integrated learning, digital education, professional skills, meta academic skills, aprendizaje computacional, aprendizaje integrado de contenidos y lenguaje, educación digital, habilidades profesionales, destrezas meta académicas
وصف الملف: application/pdf
Relation: https://www.dilemascontemporaneoseducacionpoliticayvalores.com/index.php/dilemas/article/view/1235/235; https://www.dilemascontemporaneoseducacionpoliticayvalores.com/index.php/dilemas/article/view/1235