Multi-Objective Optimization of Support Vector Machines

2006 ◽  
pp. 199-220 ◽  
Author(s):  
Thorsten Suttorp ◽  
Christian Igel
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Vector Machines

Bias and Variance Multi-objective Optimization for Support Vector Machines Model Selection

2013 ◽  
pp. 108-116 ◽  
Author(s):  
Alejandro Rosales-Pérez ◽  
Hugo Jair Escalante ◽  
Jesus A. Gonzalez ◽  
Carlos A. Reyes-Garcia ◽  
Carlos A. Coello Coello
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Vector Machines

RECENT ADVANCES IN THE MOBJ ALGORITHM FOR TRAINING ARTIFICIAL NEURAL NETWORKS

2001 ◽  
Vol 11 (03) ◽  
pp. 265-270 ◽  
Author(s):  
ROSELITO DE ALBUQUERQUE TEIXEIRA ◽  
ANTÔNIO PADUA BRAGA ◽  
RICARDO H. C. TAKAHASHI ◽  
RODNEY R. SALDANHA
Keyword(s):  
Relaxation Method ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Weight Decay ◽  
Multi Objective ◽  
Vector Machines ◽  
Regression Problems ◽  
Training Error ◽  
Network Weight ◽  
The One

This paper presents a new scheme for training MLPs which employs a relaxation method for multi-objective optimization. The algorithm works by obtaining a reduced set of solutions, from which the one with the best generalization is selected. This approach allows balancing between the training error and norm of network weight vectors, which are the two objective functions of the multi-objective optimization problem. The method is applied to classification and regression problems and compared with Weight Decay (WD), Support Vector Machines (SVMs) and standard Backpropagation (BP). It is shown that the systematic procedure for training proposed results on good generalization neural models, and outperforms traditional methods.

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