يعرض 1 - 20 نتائج من 215 نتيجة بحث عن '"Aprendizaje computacional"', وقت الاستعلام: 0.90s تنقيح النتائج
  1. 1
    Dissertation/ Thesis
  2. 2
    Dissertation/ 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)

    وصف الملف: application/pdf

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  4. 4
    Conference
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    Conference
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    Book
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    Academic Journal
  8. 8
    Academic Journal

    المصدر: Eco Matemático; Vol. 13 Núm. 1 (2022): Enero - Junio; 34-45 ; 2462-8794 ; 1794-8231

    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

  9. 9
    Dissertation/ Thesis

    Thesis Advisors: Ingeniería de Sistemas y Automática, Muñoz Martínez, Víctor Fernando, García Morales, Isabel

  10. 10
    Dissertation/ Thesis

    Thesis Advisors: Iñesta Quereda, José Manuel, Rizo Valero, David, Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos

  11. 11
    Academic Journal
  12. 12
    Book
  13. 13
    Dissertation/ 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

    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/

  14. 14
    Dissertation/ Thesis
  15. 15
    Academic 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

    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. 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    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

    وصف الملف: application/pdf

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