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» On learning algorithm selection for classification
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ICML
1996
IEEE
16 years 7 months ago
Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian ne...
Kazuo J. Ezawa, Moninder Singh, Steven W. Norton
ICML
2002
IEEE
16 years 7 months ago
Pruning Improves Heuristic Search for Cost-Sensitive Learning
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to ...
Valentina Bayer Zubek, Thomas G. Dietterich
IJCV
2008
241views more  IJCV 2008»
15 years 6 months ago
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
We investigate the role of sparsity and localized features in a biologically-inspired model of visual object classification. As in the model of Serre, Wolf, and Poggio, we first a...
Jim Mutch, David G. Lowe
TNN
2010
173views Management» more  TNN 2010»
15 years 1 months ago
Multiclass relevance vector machines: sparsity and accuracy
Abstract--In this paper we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multi-class multi-kernel Relevance Vecto...
Ioannis Psorakis, Theodoros Damoulas, Mark A. Giro...
FUZZIEEE
2007
IEEE
16 years 1 months ago
Survey of Rough and Fuzzy Hybridization
— This paper provides a broad overview of logical and black box approaches to fuzzy and rough hybridization. The logical approaches include theoretical, supervised learning, feat...
Pawan Lingras, Richard Jensen