✦ LIBER ✦

Learning a generative classifier from label proportions

✍ Scribed by Fan, Kai; Zhang, Hongyi; Yan, Songbai; Wang, Liwei; Zhang, Wensheng; Feng, Jufu

Book ID: 122252864
Publisher: Elsevier Science
Year: 2014
Tongue: English
Weight: 490 KB
Volume: 139
Category: Article
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2013.09.057

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Learning Bayesian network classifiers fr

Learning Bayesian network classifiers from label proportions

✍ Hernández-González, Jerónimo; Inza, Iñaki; Lozano, Jose A. 📂 Article 📅 2013 🏛 Elsevier Science 🌐 English ⚖ 691 KB

Learning classifier systems from a reinf

Learning classifier systems from a reinforcement learning perspective

✍ P. L. Lanzi 📂 Article 📅 2002 🏛 Springer 🌐 English ⚖ 206 KB

Capturing correlations of multiple label

Capturing correlations of multiple labels: A generative probabilistic model for multi-label learning

✍ Haiping Ma; Enhong Chen; Linli Xu; Hui Xiong 📂 Article 📅 2012 🏛 Elsevier Science 🌐 English ⚖ 458 KB

A Multistrategy Approach to Classifier L

A Multistrategy Approach to Classifier Learning from Time Series

✍ William H. Hsu; Sylvian R. Ray; David C. Wilkins 📂 Article 📅 2000 🏛 Springer 🌐 English ⚖ 395 KB

A generalized S–K algorithm for learning

A generalized S–K algorithm for learning ν-SVM classifiers

✍ Qing Tao; Gao-wei Wu; Jue Wang 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 278 KB

The S-K algorithm (Schlesinger-Kozinec algorithm) and the modified kernel technique due to Friess et al. have been recently combined to solve SVM with L 2 cost function. In this paper, we generalize S-K algorithm to be applied for soft convex hulls. As a result, our algorithm can solve m-SVM based o

A Learning Generalization Bound with an

A Learning Generalization Bound with an Application to Sparse-Representation Classifiers

✍ Yoram Gat 📂 Article 📅 2001 🏛 Springer 🌐 English ⚖ 45 KB