Dissertation/ Thesis
Multi-label Classification with Hard-/soft-decoded Error-correcting Codes
| العنوان: | Multi-label Classification with Hard-/soft-decoded Error-correcting Codes |
|---|---|
| Alternate Title: | 剛性與柔性解碼之錯誤更正碼於多標籤分類學習之應用 |
| المؤلفون: | Chun-Sung Ferng, 馮俊菘 |
| Thesis Advisors: | Hsuan-Tien Lin, 林軒田 |
| سنة النشر: | 2012 |
| المجموعة: | National Digital Library of Theses and Dissertations in Taiwan |
| الوصف: | 100 We formulate a framework for applying error-correcting codes (ECC) on multi-label classification problems. The framework treats some base learners as noisy channels and uses ECC to correct the prediction errors made by the learners. An immediate use of the framework is a novel ECC-based explanation of the popular random k-label-sets (RAKEL) algorithm using a simple repetition ECC. Using the framework, we empirically compare a broad spectrum of ECC designs for multi-label classification. The results not only demonstrate that RAKEL can be improved by applying some stronger ECC, but also show that the traditional Binary Relevance approach can be enhanced by learning more parity-checking labels. Our study on different ECC also helps understand the trade-off between the strength of ECC and the hardness of the base learning tasks. Furthermore, we extend our study to linear ECC for either hard (binary) or soft (real-valued) bits, and design a novel decoder for the ECC. We demonstrate that the decoder improves the performance of our framework. |
| Original Identifier: | 100NTU05392022 |
| نوع الوثيقة: | 學位論文 ; thesis |
| وصف الملف: | 58 |
| الاتاحة: | http://ndltd.ncl.edu.tw/handle/16421654083627886596 |
| رقم الانضمام: | edsndl.TW.100NTU05392022 |
| قاعدة البيانات: | Networked Digital Library of Theses & Dissertations |
| الوصف غير متاح. |