Stopping Conditions for Exact Computation of Leave-One-Out Error in Support Vector Machines Vojtˇech Franc1 VOJTECH . FRANC @ FIRST. FRAUNHOFER . DE Pavel Laskov1,2

Source URL: www-rsec.cs.uni-tuebingen.de

APPROXIMATE LEAVE-ONE-OUT ERROR ESTIMATION FOR LEARNING WITH SMOOTH, STRICTLY CONVEX MARGIN LOSS FUNCTIONS Christopher P. Diehl Applied Physics Laboratory

Source URL: www.cpdiehl.org

Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM Yann Guermeur LORIA-CNRS Campus Scientifique, BPVandœuvre-l`es-Nancy cedex, France

Source URL: www.loria.fr

• Operator theory
• Hilbert space
• Statistical classification
• Linear algebra
• Support vector machine
• Reproducing kernel Hilbert space
• Function
• Convex function
• Positive-definite kernel
• Mathematics
• Mathematical analysis
• Statistics

Radius-Margin Bound on the Leave-One-Out Error of the LLW-M-SVM Yann Guermeur1 , Emmanuel Monfrini2 1 2

Source URL: www.loria.fr

• Mathematical analysis
• Statistical classification
• Hilbert space
• Linear algebra
• Support vector machine
• Reproducing kernel Hilbert space
• Inner product space
• Positive-definite kernel
• Least squares support vector machine
• Algebra
• Operator theory
• Mathematics

25.3 n&v MH[removed]:59 pm

Source URL: cbcl.mit.edu

• Stability
• Computational learning theory
• Supervised learning
• Empirical risk minimization
• Vapnik–Chervonenkis theory
• Leave one out error
• Machine learning
• Learning
• Artificial intelligence

Statistical Learning : stability is sufficient for generalization and necessary and sufficient for consistency of Empirical Risk Minimization Sayan Mukherjee †
, Partha Niyogi⊥ , Tomaso Poggio†1 and Ryan Rifkin †

Source URL: cbcl.mit.edu

• Learning
• Stability
• Statistics
• Leave one out error
• Computational learning theory
• Probability theory
• Empirical risk minimization
• Supervised learning
• Convergence of random variables
• Mathematics
• Mathematical analysis
• Machine learning