983.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: svm<5>Text Categorization
发信站: 南京大学小百合站 (Tue Jun  4 18:43:36 2002), 站内信件

Text Categorization
Text categorization is the assignment of natural language texts to one or
 more predefined categories based on their content. Applications include:
 assigning subject categories to documents to support text retrieval, routing
, and filtering; email or files sorting into folder hierarchies; web page
 sorting into search engine categories. 
Reference(s): 
Text Categorization with Support Vector Machines: Learning with Many Relevant
 Features. 
T. Joachims, 
European Conference on Machine Learning (ECML), 
1998. 
Inductive Learning Algorithms and Representations for Text Categorization
, 
S. Dumais, J. Platt, D. Heckerman, M. Sahami, 
7th International Conference on Information and Knowledge Management, 
1998. 
Support Vector Machines for Spam Categorization. H. Drucker, with D. Wu and
 V. Vapnik. IEEE Trans. on Neural Networks , vol 10, number 5, pp. 1048-1054
. 1999. 
Transductive Inference for Text Classification using Support Vector Machines
. 
Thorsten Joachims. 
International Conference on Machine Learning (ICML), 
1999.
Reference link(s): 
Joachims-98 Postcript, Joachims-98 PDF 
Dumais et al 98 
Drucker et al 98 
Joachins-99 Postcript 
Joachims-99 PDF 
Data link(s): 
Reuters-21578
Entered by: Isabelle Guyon <[e-mail]> - Friday, September 17
, 1999 at 15:19:48 (PDT). Last modified, October 13, 1999. 
Comments: Joachims-98 reports that SVMs are well suited to learn in very 
high dimensional spaces (> 10000 inputs). They achieve substantial improvements
 over the currently best performing methods, eliminating the need for feature
 selection. The tests were run on the Oshumed corpus of William Hersh and
 Reuter-21578. Dumais et al report that they use linear SVMs because they
 are both accurate and fast (to train and to use). They are 35 times faster
 to train that the next most accurate classifier that they tested (Decision
 Trees). They have applied SVMs to the Reuter-21578 collection, emails and
 web pages. Drucker at al classify emails as spam and non spam. They find
 that boosting trees and SVMs have similar performance in terms of accuracy
 and speed. SVMs train significatly faster. Joachims-99 report that transduction
 is a very natural setting for many text classification and information retri
eval
 tasks. Transductive SVMs improve performance especially in cases with very
 small amounts of labelled training data. 
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