A new method of training samples choose for support vector machines

2010 ◽  
Author(s):  
Dezheng Zhu ◽  
Jiafu Jiang
Keyword(s):  
Support Vector Machines ◽  
New Method ◽  
Support Vector ◽  
Training Samples ◽  
Vector Machines

scholarly journals Single Directional SMO Algorithm for Least Squares Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Xigao Shao ◽  
Kun Wu ◽  
Bifeng Liao
Keyword(s):  
Support Vector Machines ◽  
Least Squares ◽  
Decomposition Methods ◽  
New Method ◽  
Support Vector ◽  
Major Step ◽  
Vector Machines ◽  
Working Set ◽  
A New Technique ◽  
Smo Algorithm

Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.

scholarly journals Cross-Validation, Bootstrap, and Support Vector Machines

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Masaaki Tsujitani ◽  
Yusuke Tanaka
Keyword(s):  
Support Vector Machines ◽  
Goodness Of Fit ◽  
Cross Validation ◽  
Prediction Rule ◽  
Support Vector ◽  
Resampling Methods ◽  
Tuning Parameters ◽  
Training Samples ◽  
Vector Machines ◽  
Disease Study

This paper considers the applications of resampling methods to support vector machines (SVMs). We take into account the leaving-one-out cross-validation (CV) when determining the optimum tuning parameters and bootstrapping the deviance in order to summarize the measure of goodness-of-fit in SVMs. The leaving-one-out CV is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. We analyze the data from a mackerel-egg survey and a liver-disease study.

Soft urban water cover extraction using mixed training samples and Support Vector Machines

2015 ◽  
Vol 36 (13) ◽  
pp. 3331-3344 ◽  
Author(s):  
Xiaoxia Sun ◽  
Liwei Li ◽  
Bing Zhang ◽  
Dongmei Chen ◽  
Lianru Gao
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Urban Water ◽  
Training Samples ◽  
Vector Machines ◽  
Water Cover

A New Method for Sensorless Estimation of the Speed and Position in Brushed DC Motors Using Support Vector Machines

2012 ◽  
Vol 59 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Ernesto Vazquez-Sanchez ◽  
Jaime Gomez-Gil ◽  
José Carlos Gamazo-Real ◽  
José Fernando Diez-Higuera
Keyword(s):  
Support Vector Machines ◽  
New Method ◽  
Support Vector ◽  
Dc Motors ◽  
Vector Machines

A Rough Margin Based Fuzzy Support Vector Machine

2011 ◽  
Vol 204-210 ◽  
pp. 879-882
Author(s):  
Kai Li ◽  
Xiao Xia Lu
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machines ◽  
Rough Set ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Lower Margin ◽  
Training Samples ◽  
Separating Hyperplane ◽  
Vector Machines ◽  
Benchmark Datasets

By combining fuzzy support vector machine with rough set, we propose a rough margin based fuzzy support vector machine (RFSVM). It inherits the characteristic of the FSVM method and considers position of training samples of the rough margin in order to reduce overfitting due to noises or outliers. The new proposed algorithm finds the optimal separating hyperplane that maximizes the rough margin containing lower margin and upper margin. Meanwhile, the points lied on the lower margin have larger penalty than these in the boundary of the rough margin. Experiments on several benchmark datasets show that the RFSVM algorithm is effective and feasible compared with the existing support vector machines.

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