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AAAI
2011
12 years 9 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
TNN
2008
97views more  TNN 2008»
13 years 9 months ago
Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm
Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
Liefeng Bo, Ling Wang, Licheng Jiao
ICPR
2006
IEEE
14 years 10 months ago
Mixture of Support Vector Machines for HMM based Speech Recognition
Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-opt...
Sven E. Krüger, Martin Schafföner, Marce...
BIBM
2008
IEEE
137views Bioinformatics» more  BIBM 2008»
14 years 3 months ago
Exploring Alternative Splicing Features Using Support Vector Machines
Alternative splicing is a mechanism for generating different gene transcripts (called isoforms) from the same genomic sequence. Finding alternative splicing events experimentally ...
Jing Xia, Doina Caragea, Susan Brown
ICASSP
2008
IEEE
14 years 3 months ago
Nested support vector machines
The one-class and cost-sensitive support vector machines (SVMs) are state-of-the-art machine learning methods for estimating density level sets and solving weighted classificatio...
Gyemin Lee, Clayton Scott