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» Learning of Boolean Functions Using Support Vector Machines
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SDM
2010
SIAM
226views Data Mining» more  SDM 2010»
13 years 9 months ago
Two-View Transductive Support Vector Machines
Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications, especially for Internet classification tasks like review spam...
Guangxia Li, Steven C. H. Hoi, Kuiyu Chang
JMLR
2010
121views more  JMLR 2010»
13 years 2 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
ICASSP
2011
IEEE
12 years 11 months ago
Feature selection based on Multiple Kernel Learning for single-channel sound source localization using the acoustic transfer fun
This paper presents a sound source (talker) localization method using only a single microphone. In our previous work [1], we discussed the single-channel sound source localization...
Ryoichi Takashima, Tetsuya Takiguchi, Yasuo Ariki
BMCBI
2007
127views more  BMCBI 2007»
13 years 7 months ago
Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
Background: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The no...
Jian Tian, Ningfeng Wu, Xuexia Guo, Jun Guo, Juhua...
ACL
2009
13 years 5 months ago
A Novel Discourse Parser Based on Support Vector Machine Classification
This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statisti...
David duVerle, Helmut Prendinger