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IJCNN
2008
IEEE
14 years 1 months ago
Sparse support vector machines trained in the reduced empirical feature space
— We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
Kazuki Iwamura, Shigeo Abe
SIGMOD
2004
ACM
140views Database» more  SIGMOD 2004»
14 years 7 months ago
SoundCompass: A Practical Query-by-Humming System
This paper describes our practical query-by-humming system, SoundCompass, which is being used as a karaoke song selection system in Japan. First, we describe the fundamental techn...
Naoko Kosugi, Yasushi Sakurai, Masashi Morimoto
BMCBI
2005
131views more  BMCBI 2005»
13 years 7 months ago
Regularized Least Squares Cancer Classifiers from DNA microarray data
Background: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information ...
Nicola Ancona, Rosalia Maglietta, Annarita D'Addab...
BMCBI
2004
114views more  BMCBI 2004»
13 years 7 months ago
Profiled support vector machines for antisense oligonucleotide efficacy prediction
Background: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises ...
Gustavo Camps-Valls, Alistair M. Chalk, Antonio J....
JMLR
2008
110views more  JMLR 2008»
13 years 7 months ago
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluat...
Bo Jiang, Xuegong Zhang, Tianxi Cai