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» Robust Feature Selection Algorithms
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113
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GECCO
1999
Springer
133views Optimization» more  GECCO 1999»
15 years 6 months ago
Non-Standard Crossover for a Standard Representation - Commonality-Based Feature Subset Selection
The Commonality-Based Crossover Framework has been presented as a general model for designing problem specific operators. Following this model, the Common Features/Random Sample ...
Stephen Y. Chen, Cesar Guerra-Salcedo, Stephen F. ...
127
Voted
ICIP
2006
IEEE
16 years 3 months ago
Perceptual Feature Selection for Semantic Image Classification
Content-based image retrieval has become an indispensable tool for managing the rapidly growing collections of digital images. The goal is to organize the contents semantically, a...
Dejan Depalov, Thrasyvoulos N. Pappas, Dongge Li, ...
113
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KDD
2006
ACM
155views Data Mining» more  KDD 2006»
16 years 2 months ago
Single-pass online learning: performance, voting schemes and online feature selection
To learn concepts over massive data streams, it is essential to design inference and learning methods that operate in real time with limited memory. Online learning methods such a...
Vitor R. Carvalho, William W. Cohen
149
Voted
IEEEPACT
2008
IEEE
15 years 8 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...
120
Voted
IPMU
2010
Springer
15 years 20 days 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