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» Feature Selection for Support Vector Machines
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JMLR
2006
89views more  JMLR 2006»
13 years 7 months ago
Maximum-Gain Working Set Selection for SVMs
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
Tobias Glasmachers, Christian Igel
ICASSP
2009
IEEE
14 years 2 months ago
Joint map adaptation of feature transformation and Gaussian Mixture Model for speaker recognition
This paper extends our previous work on feature transformationbased support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (...
Donglai Zhu, Bin Ma, Haizhou Li
ICPR
2010
IEEE
13 years 6 months ago
Human Action Recognition Using Segmented Skeletal Features
We present a novel human action recognition system based on segmented skeletal features which are separated into several human body parts such as face, torso and limbs. Our propos...
Sang Min Yoon, Arjan Kuijper
ECRIME
2007
13 years 11 months ago
A comparison of machine learning techniques for phishing detection
There are many applications available for phishing detection. However, unlike predicting spam, there are only few studies that compare machine learning techniques in predicting ph...
Saeed Abu-Nimeh, Dario Nappa, Xinlei Wang, Suku Na...
EMO
2009
Springer
147views Optimization» more  EMO 2009»
14 years 2 months ago
Application of MOGA Search Strategy to SVM Training Data Selection
When training Support Vector Machine (SVM), selection of a training data set becomes an important issue, since the problem of overfitting exists with a large number of training da...
Tomoyuki Hiroyasu, Masashi Nishioka, Mitsunori Mik...