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ICCV
2011
IEEE
7 years 8 months ago
Struck: Structured Output Tracking with Kernels
Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task a...
Sam Hare, Amir Saffari, Philip H.S. Torr
SCHOLARPEDIA
2008
89views more  SCHOLARPEDIA 2008»
8 years 6 months ago
Support vector clustering
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to d...
Asa Ben-Hur
OL
2007
103views more  OL 2007»
8 years 8 months ago
Support vector machine via nonlinear rescaling method
In this paper we construct the linear support vector machine (SVM) based on the nonlinear rescaling (NR) methodology (see [9, 11, 10] and references therein). The formulation of t...
Roman A. Polyak, Shen-Shyang Ho, Igor Griva
NECO
1998
83views more  NECO 1998»
8 years 8 months ago
Properties of Support Vector Machines
Support Vector Machines (SVMs) perform pattern recognition between two point classes by nding a decision surface determined by certain points of the training set, termed Support V...
Massimiliano Pontil, Alessandro Verri
PR
2006
111views more  PR 2006»
8 years 8 months ago
An adaptive error penalization method for training an efficient and generalized SVM
A novel training method has been proposed for increasing efficiency and generalization of support vector machine (SVM). The efficiency of SVM in classification is directly determi...
Yiqiang Zhan, Dinggang Shen
FSS
2007
102views more  FSS 2007»
8 years 8 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...
JMLR
2006
150views more  JMLR 2006»
8 years 8 months ago
Building Support Vector Machines with Reduced Classifier Complexity
Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overc...
S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCos...
JMLR
2006
96views more  JMLR 2006»
8 years 8 months ago
A Hierarchy of Support Vector Machines for Pattern Detection
We introduce a computational design for pattern detection based on a tree-structured network of support vector machines (SVMs). An SVM is associated with each cell in a recursive ...
Hichem Sahbi, Donald Geman
CSL
2006
Springer
8 years 8 months ago
Support vector machines for speaker and language recognition
Support vector machines (SVMs) have proven to be a powerful technique for pattern classification. SVMs map inputs into a high dimensional space and then separate classes with a hy...
William M. Campbell, Joseph P. Campbell, Douglas A...
CORR
2008
Springer
114views Education» more  CORR 2008»
8 years 8 months ago
Support Vector Machine Classification with Indefinite Kernels
In this paper, we propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our...
Ronny Luss, Alexandre d'Aspremont
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