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KDD
2005
ACM
168views Data Mining» more  KDD 2005»
16 years 27 days ago
Nomograms for visualizing support vector machines
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan B...
124
Voted
NIPS
2004
15 years 1 months ago
Machine Learning Applied to Perception: Decision Images for Gender Classification
We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We r...
Felix A. Wichmann, Arnulf B. A. Graf, Eero P. Simo...
WCE
2007
15 years 1 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
127
Voted
TNN
2010
143views Management» more  TNN 2010»
14 years 7 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
94
Voted
FLAIRS
2001
15 years 1 months ago
Improvement of Nearest-Neighbor Classifiers via Support Vector Machines
Theoretically well-founded, Support Vector Machines (SVM)are well-knownto be suited for efficiently solving classification problems. Althoughimprovedgeneralization is the maingoal...
Marc Sebban, Richard Nock