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» Predicting Time Series with Support Vector Machines
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146
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JMLR
2011
110views more  JMLR 2011»
14 years 10 months ago
Training SVMs Without Offset
We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stoppi...
Ingo Steinwart, Don R. Hush, Clint Scovel
152
Voted
ECML
2006
Springer
15 years 7 months ago
Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees
In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kerne...
Alessandro Moschitti
126
Voted
BMCBI
2008
116views more  BMCBI 2008»
15 years 3 months ago
The combination approach of SVM and ECOC for powerful identification and classification of transcription factor
Background: Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network co...
Guangyong Zheng, Ziliang Qian, Qing Yang, Chaochun...
114
Voted
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
16 years 4 months ago
Multi-focal learning and its application to customer service support
In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within...
Yong Ge, Hui Xiong, Wenjun Zhou, Ramendra K. Sahoo...
136
Voted
CIKM
2009
Springer
15 years 10 months ago
Large margin transductive transfer learning
Recently there has been increasing interest in the problem of transfer learning, in which the typical assumption that training and testing data are drawn from identical distributi...
Brian Quanz, Jun Huan