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» Feature selection for ranking using boosted trees
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IPMU
2010
Springer
14 years 10 months ago
Attribute Value Selection Considering the Minimum Description Length Approach and Feature Granularity
Abstract. In this paper we introduce a new approach to automatic attribute and granularity selection for building optimum regression trees. The method is based on the minimum descr...
Kemal Ince, Frank Klawonn
ICASSP
2010
IEEE
14 years 12 months ago
Boosted binary features for noise-robust speaker verification
The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a nov...
Anindya Roy, Mathew Magimai-Doss, Sébastien...
ICML
1999
IEEE
16 years 15 days ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting
CVPR
2006
IEEE
16 years 1 months ago
Supervised Learning of Edges and Object Boundaries
Edge detection is one of the most studied problems in computer vision, yet it remains a very challenging task. It is difficult since often the decision for an edge cannot be made ...
Piotr Dollár, Zhuowen Tu, Serge Belongie
KDD
2004
ACM
330views Data Mining» more  KDD 2004»
16 years 3 days ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof