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» Optimal feature selection for support vector machines
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AAAI
2011
12 years 7 months ago
Improving Semi-Supervised Support Vector Machines Through Unlabeled Instances Selection
Semi-supervised support vector machines (S3VMs) are a kind of popular approaches which try to improve learning performance by exploiting unlabeled data. Though S3VMs have been fou...
Yu-Feng Li, Zhi-Hua Zhou
ICPR
2004
IEEE
14 years 8 months ago
Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video
This paper presents a novel face detection method in video by using Discriminating Feature Analysis (DFA) and Support Vector Machine (SVM). Our method first incorporates temporal ...
Chengjun Liu, Peichung Shih
BMCBI
2007
147views more  BMCBI 2007»
13 years 7 months ago
Improved residue contact prediction using support vector machines and a large feature set
Background: Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In s...
Jianlin Cheng, Pierre Baldi
CSDA
2007
105views more  CSDA 2007»
13 years 7 months ago
Model selection for support vector machines via uniform design
The problem of choosing a good parameter setting for a better generalization performance in a learning task is the so-called model selection. A nested uniform design (UD) methodol...
Chien-Ming Huang, Yuh-Jye Lee, Dennis K. J. Lin, S...
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 1 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi