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» Support Vector Regression Using Mahalanobis Kernels
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ML
2010
ACM
181views Machine Learning» more  ML 2010»
13 years 7 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
ICCV
2001
IEEE
14 years 10 months ago
Shape Deformation: SVM Regression and Application to Medical Image Segmentation
This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification o...
Song Wang, Weiyu Zhu, Zhi-Pei Liang
ESANN
2000
13 years 10 months ago
Support Vector Committee Machines
Abstract. This paper proposes a mathematical programming framew ork for combining SVMs with possibly di erent kernels. Compared to single SVMs, the advantage of this approach is tw...
Dominique Martinez, Gilles Millerioux
GECCO
2007
Springer
184views Optimization» more  GECCO 2007»
14 years 21 days ago
Evolving kernels for support vector machine classification
While support vector machines (SVMs) have shown great promise in supervised classification problems, researchers have had to rely on expert domain knowledge when choosing the SVM&...
Keith Sullivan, Sean Luke
ICIAP
2009
ACM
14 years 9 months ago
Estimation of Object Position Based on Color and Shape Contextual Information
This paper presents a method to estimate the position of object using contextual information. Although convention methods used only shape contextual information, color contextual i...
Takashi Ishihara, Kazuhiro Hotta, Haruhisa Takahas...