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MCS
2007
Springer
14 years 3 months ago
An Ensemble Approach for Incremental Learning in Nonstationary Environments
Abstract. We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presente...
Michael Muhlbaier, Robi Polikar
ANLP
1997
116views more  ANLP 1997»
13 years 11 months ago
A Maximum Entropy Approach to Identifying Sentence Boundaries
We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of., ...
Jeffrey C. Reynar, Adwait Ratnaparkhi
SIGSOFT
2007
ACM
14 years 10 months ago
Training on errors experiment to detect fault-prone software modules by spam filter
The fault-prone module detection in source code is of importance for assurance of software quality. Most of previous fault-prone detection approaches are based on software metrics...
Osamu Mizuno, Tohru Kikuno
ICML
2002
IEEE
14 years 10 months ago
Non-Disjoint Discretization for Naive-Bayes Classifiers
Previous discretization techniques have discretized numeric attributes into disjoint intervals. We argue that this is neither necessary nor appropriate for naive-Bayes classifiers...
Ying Yang, Geoffrey I. Webb
ICMCS
2006
IEEE
192views Multimedia» more  ICMCS 2006»
14 years 3 months ago
Classifier Optimization for Multimedia Semantic Concept Detection
In this paper, we present an AUC (i.e., the Area Under the Curve of Receiver Operating Characteristics (ROC)) maximization based learning algorithm to design the classifier for ma...
Sheng Gao, Qibin Sun