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» Support Vector Regression Using Mahalanobis Kernels
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ICASSP
2011
IEEE
13 years 17 days ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
CVPR
2006
IEEE
14 years 11 months ago
Incorporating the Boltzmann Prior in Object Detection Using SVM
In this paper we discuss object detection when only a small number of training examples are given. Specifically, we show how to incorporate a simple prior on the distribution of n...
Margarita Osadchy, Daniel Keren
GECCO
2008
Springer
177views Optimization» more  GECCO 2008»
13 years 10 months ago
Reduced computation for evolutionary optimization in noisy environment
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
Maumita Bhattacharya
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
14 years 15 days ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
SMC
2007
IEEE
133views Control Systems» more  SMC 2007»
14 years 3 months ago
Text classification using multi-word features
—We carried out a series of experiments on text classification using multi-word features. An automated method was proposed to extract the multi-words from text data set and two d...
Wen Zhang, Taketoshi Yoshida, Xijin Tang