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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
15 years 9 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
16 years 4 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos
LATA
2010
Springer
15 years 11 months ago
Three Learnable Models for the Description of Language
Abstract. Learnability is a vital property of formal grammars: representation classes should be defined in such a way that they are learnable. One way to build learnable represent...
Alexander Clark
KDD
2004
ACM
123views Data Mining» more  KDD 2004»
16 years 4 months ago
A DEA approach for model combination
This paper proposes a novel Data Envelopment Analysis (DEA) based approach for model combination. We first prove that for the 2-class classification problems DEA models identify t...
Zhiqiang Zheng, Balaji Padmanabhan, Haoqiang Zheng
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 4 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...