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» Infinite Ensemble Learning with Support Vector Machines
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IDEAL
2005
Springer
15 years 10 months ago
Exploiting Sequence Dependencies in the Prediction of Peroxisomal Proteins
Prediction of peroxisomal matrix proteins generally depends on the presence of one of two distinct motifs at the end of the amino acid sequence. PTS1 peroxisomal proteins have a we...
Mark Wakabayashi, John Hawkins, Stefan Maetschke, ...
ICML
2008
IEEE
16 years 5 months ago
Training SVM with indefinite kernels
Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the prob...
Jianhui Chen, Jieping Ye
LWA
2004
15 years 5 months ago
A Simple Method For Estimating Conditional Probabilities For SVMs
Support Vector Machines (SVMs) have become a popular learning algorithm, in particular for large, high-dimensional classification problems. SVMs have been shown to give most accur...
Stefan Rüping
ICPR
2008
IEEE
16 years 5 months ago
A discrete-time parallel update algorithm for distributed learning
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithm...
Christian Bauckhage, Tansu Alpcan
ICML
2005
IEEE
16 years 5 months ago
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based ...
Qiang Sun, Gerald DeJong