Tackling the Poor Assumptions of Naive Bayes Text Classifiers

First Page		Document Content
Date: 2006-01-11 19:03:07 Machine learning Naive Bayes classifier Multiclass classification Linear classifier Document classification Classification rule Random naive Bayes Averaged one-dependence estimators Statistics Statistical classification Bayesian statistics		Tackling the Poor Assumptions of Naive Bayes Text Classifiers Add to Reading List Source URL: www.aaai.org Download Document from Source Website File Size: 231,85 KB Share Document on Facebook

	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 1 Multi-Dimensional Classification with Super-Classes Jesse Read, Concha Bielza, Member, IEEE, Pedro Larra˜naga, Member, IEEE, DocID: 1rnBc - View Document
	Multi-label Classification with Classifier Chains Jesse Read Aalto University School of Science, Department of Information and Computer Science and Helsinki Institute for Information Technology Helsinki, Finland DocID: 1rder - View Document
	On-line Multi-label Classification A Problem Transformation Approach Jesse Read Supervisors: Bernhard Pfahringer, Geoff Holmes DocID: 1ragU - View Document
	Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems Cheng-Lin Liu National Laboratory of Pattern Recognition (NLPR) Institute of Automation, Chinese Academy of Sciences P.O DocID: 1r88X - View Document
	Appendix A. Introduction to Binary Support Vector Machines Below we summarize the main ideas behind binary Support Vector Machines via a short theoretical description followed by an example. For a detailed review of SVMs DocID: 1qWBp - View Document