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DATAMINE
1998
145views more  DATAMINE 1998»
13 years 7 months ago
A Tutorial on Support Vector Machines for Pattern Recognition
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
Christopher J. C. Burges
IJCNN
2006
IEEE
14 years 1 months ago
Comparing Kernels for Predicting Protein Binding Sites from Amino Acid Sequence
— The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has importan...
Feihong Wu
DAC
2003
ACM
14 years 9 months ago
Support vector machines for analog circuit performance representation
The use of Support Vector Machines (SVMs) to represent the performance space of analog circuits is explored. In abstract terms, an analog circuit maps a set of input design parame...
Fernando De Bernardinis, Michael I. Jordan, Albert...
CVPR
2010
IEEE
13 years 11 months ago
Large-Scale Image Categorization with Explicit Data Embedding
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
Florent Perronnin, Jorge Sanchez, Yan Liu
FSS
2007
102views more  FSS 2007»
13 years 7 months ago
Extraction of fuzzy rules from support vector machines
The relationship between support vector machines (SVMs) and Takagi–Sugeno–Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for ...
Juan Luis Castro, L. D. Flores-Hidalgo, Carlos Jav...