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ICASSP
2007
IEEE
14 years 1 months ago
A Self-Training Semi-Supervised Support Vector Machine Algorithm and its Applications in Brain Computer Interface
In this paper, we analyze the convergence of an iterative selftraining semi-supervised support vector machine (SVM) algorithm, which is designed for classi cation in small trainin...
Yuanqing Li, Huiqi Li, Cuntai Guan, Zhengyang Chin
ICDM
2002
IEEE
133views Data Mining» more  ICDM 2002»
14 years 12 days ago
Learning with Progressive Transductive Support Vector Machine
Support vector machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead ...
Yisong Chen, Guoping Wang, Shihai Dong
BIBM
2008
IEEE
172views Bioinformatics» more  BIBM 2008»
14 years 1 months ago
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
Protein fold recognition is the prediction of protein’s tertiary structure (Fold) given the protein’s sequence without relying on sequence similarity. Using machine learning t...
Yazhene Krishnaraj, Chandan K. Reddy
CVPR
2008
IEEE
14 years 9 months ago
Enforcing non-positive weights for stable support vector tracking
In this paper we demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted E...
Simon Lucey
CVPR
2008
IEEE
14 years 9 months ago
Efficient sequential correspondence selection by cosegmentation
In many retrieval, object recognition and wide baseline stereo methods, correspondences of interest points are established possibly sublinearly by matching a compact descriptor su...
Jan Cech, Jiri Matas, Michal Perdoch