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CIKM
2008
Springer
13 years 10 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles
JMLR
2010
161views more  JMLR 2010»
13 years 2 months ago
Feature Selection for Text Classification Based on Gini Coefficient of Inequality
A number of feature selection mechanisms have been explored in text categorization, among which mutual information, information gain and chi-square are considered most effective. ...
Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. G...
IJDLS
2010
108views more  IJDLS 2010»
13 years 5 months ago
Sampling the Web as Training Data for Text Classification
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always b...
Wei-Yen Day, Chun-Yi Chi, Ruey-Cheng Chen, Pu-Jen ...
ICML
1998
IEEE
14 years 8 months ago
Employing EM and Pool-Based Active Learning for Text Classification
This paper shows how a text classifier's need for labeled training documents can be reduced by taking advantage of a large pool of unlabeled documents. We modify the Query-by...
Andrew McCallum, Kamal Nigam
BMCBI
2008
114views more  BMCBI 2008»
13 years 8 months ago
Combining classifiers for improved classification of proteins from sequence or structure
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
Iain Melvin, Jason Weston, Christina S. Leslie, Wi...