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ML
2002
ACM
107views Machine Learning» more  ML 2002»
13 years 7 months ago
Training Invariant Support Vector Machines
Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated...
Dennis DeCoste, Bernhard Schölkopf
ICML
2004
IEEE
14 years 8 months ago
Support vector machine learning for interdependent and structured output spaces
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
ACCV
2010
Springer
13 years 2 months ago
Randomised Manifold Forests for Principal Angle-Based Face Recognition
Abstract. In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to me...
Ujwal D. Bonde, Tae-Kyun Kim, K. R. Ramakrishnan
BIBE
2003
IEEE
124views Bioinformatics» more  BIBE 2003»
14 years 1 months ago
Prediction of Contact Maps Using Support Vector Machines
Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algor...
Ying Zhao, George Karypis
PREMI
2007
Springer
14 years 1 months ago
Ensemble Approaches of Support Vector Machines for Multiclass Classification
Support vector machine (SVM) which was originally designed for binary classification has achieved superior performance in various classification problems. In order to extend it to ...
Jun-Ki Min, Jin-Hyuk Hong, Sung-Bae Cho