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KDD
2004
ACM
139views Data Mining» more  KDD 2004»
16 years 6 days ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
INTERSPEECH
2010
14 years 6 months ago
Combining five acoustic level modeling methods for automatic speaker age and gender recognition
This paper presents a novel automatic speaker age and gender identification approach which combines five different methods at the acoustic level to improve the baseline performanc...
Ming Li, Chi-Sang Jung, Kyu Jeong Han
ICAC
2006
IEEE
15 years 5 months ago
Fast and Effective Worm Fingerprinting via Machine Learning
— As Internet worms become ever faster and more sophisticated, it is important to be able to extract worm signatures in an accurate and timely manner. In this paper, we apply mac...
Stewart M. Yang, Jianping Song, Harish Rajamani, T...
IJCNN
2006
IEEE
15 years 5 months ago
Learning the Kernel in Mahalanobis One-Class Support Vector Machines
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
Ivor W. Tsang, James T. Kwok, Shutao Li
JMLR
2006
150views more  JMLR 2006»
14 years 11 months ago
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
Support vector machines utilizing the 1-norm, typically set up as linear programs (Mangasarian, 2000; Bradley and Mangasarian, 1998), are formulated here as a completely unconstra...
Olvi L. Mangasarian