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» Unsupervised feature selection using a neuro-fuzzy approach
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AAAI
2008
15 years 2 months ago
Markov Blanket Feature Selection for Support Vector Machines
Based on Information Theory, optimal feature selection should be carried out by searching Markov blankets. In this paper, we formally analyze the current Markov blanket discovery ...
Jianqiang Shen, Lida Li, Weng-Keen Wong
CVPR
2004
IEEE
16 years 1 months ago
Detecting Unusual Activity in Video
We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selection...
Hua Zhong, Jianbo Shi, Mirkó Visontai
ICDM
2006
IEEE
164views Data Mining» more  ICDM 2006»
15 years 5 months ago
Unsupervised Learning of Tree Alignment Models for Information Extraction
We propose an algorithm for extracting fields from HTML search results. The output of the algorithm is a database table– a data structure that better lends itself to high-level...
Philip Zigoris, Damian Eads, Yi Zhang
ICML
2005
IEEE
16 years 19 days ago
Online feature selection for pixel classification
Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a...
Karen A. Glocer, Damian Eads, James Theiler
PR
2010
163views more  PR 2010»
14 years 10 months ago
Optimal feature selection for support vector machines
Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and ...
Minh Hoai Nguyen, Fernando De la Torre