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» A Training Method with Small Computation for Classification
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RECOMB
2005
Springer
14 years 8 months ago
Learning Interpretable SVMs for Biological Sequence Classification
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
Christin Schäfer, Gunnar Rätsch, Sö...
ICPR
2006
IEEE
14 years 9 months ago
A Minimum Sphere Covering Approach to Pattern Classification
In this paper we present a minimum sphere covering approach to pattern classification that seeks to construct a minimum number of spheres to represent the training data and formul...
Jigang Wang, Predrag Neskovic, Leon N. Cooper
CVPR
2006
IEEE
14 years 10 months ago
An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis
We present a novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery, and demonstrate its utility in detect...
Ayelet Akselrod-Ballin, Meirav Galun, Ronen Basri,...
ML
2008
ACM
222views Machine Learning» more  ML 2008»
13 years 7 months ago
Boosted Bayesian network classifiers
The use of Bayesian networks for classification problems has received significant recent attention. Although computationally efficient, the standard maximum likelihood learning me...
Yushi Jing, Vladimir Pavlovic, James M. Rehg
CVPR
2010
IEEE
14 years 4 months ago
Sufficient Dimensionality Reduction for Visual Sequence Classification
When classifying high-dimensional sequence data, traditional methods (e.g., HMMs, CRFs) may require large amounts of training data to avoid overfitting. In such cases dimensional...
Alex Shyr, Raquel Urtasun, Michael Jordan