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NAACL
2007
13 years 9 months ago
Using "Annotator Rationales" to Improve Machine Learning for Text Categorization
We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: an...
Omar Zaidan, Jason Eisner, Christine D. Piatko
ACL
2001
13 years 9 months ago
Using Machine Learning to Maintain Rule-based Named-Entity Recognition and Classification Systems
This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, construc...
Georgios Petasis, Frantz Vichot, Francis Wolinski,...
ICMCS
2006
IEEE
131views Multimedia» more  ICMCS 2006»
14 years 2 months ago
Self-Supervised Learning for Robust Video Indexing
The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approac...
Ralph Ewerth, Bernd Freisleben
KDD
2002
ACM
108views Data Mining» more  KDD 2002»
14 years 8 months ago
Incremental Machine Learning to Reduce Biochemistry Lab Costs in the Search for Drug Discovery
This paper promotes the use of supervised machine learning in laboratory settings where chemists have a large number of samples to test for some property, and are interested in id...
George Forman
ICANN
2001
Springer
14 years 14 days ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc