scholarly journals A Hybrid Algorithm to Improve the Accuracy of Support Vector Machines on Skewed Data-Sets

2014 ◽  
pp. 782-788 ◽  
Author(s):  
Jair Cervantes ◽  
De-Shuang Huang ◽  
Farid García-Lamont ◽  
Asdrúbal López Chau
Keyword(s):  
Support Vector Machines ◽  
Hybrid Algorithm ◽  
Support Vector ◽  
Data Sets ◽  
Skewed Data ◽  
Vector Machines

scholarly journals Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses

2016 ◽  
Vol 28 (6) ◽  
pp. 1217-1247 ◽  
Author(s):  
Yunlong Feng ◽  
Yuning Yang ◽  
Xiaolin Huang ◽  
Siamak Mehrkanoon ◽  
Johan A. K. Suykens
Keyword(s):  
Support Vector Machines ◽  
Robust Statistics ◽  
Real Data ◽  
Constructive Proof ◽  
Support Vector ◽  
Data Sets ◽  
Large Margin ◽  
Hinge Loss ◽  
Vector Machines ◽  
Smooth Classification

This letter addresses the robustness problem when learning a large margin classifier in the presence of label noise. In our study, we achieve this purpose by proposing robustified large margin support vector machines. The robustness of the proposed robust support vector classifiers (RSVC), which is interpreted from a weighted viewpoint in this work, is due to the use of nonconvex classification losses. Besides the robustness, we also show that the proposed RSCV is simultaneously smooth, which again benefits from using smooth classification losses. The idea of proposing RSVC comes from M-estimation in statistics since the proposed robust and smooth classification losses can be taken as one-sided cost functions in robust statistics. Its Fisher consistency property and generalization ability are also investigated. Besides the robustness and smoothness, another nice property of RSVC lies in the fact that its solution can be obtained by solving weighted squared hinge loss–based support vector machine problems iteratively. We further show that in each iteration, it is a quadratic programming problem in its dual space and can be solved by using state-of-the-art methods. We thus propose an iteratively reweighted type algorithm and provide a constructive proof of its convergence to a stationary point. Effectiveness of the proposed classifiers is verified on both artificial and real data sets.

Modelling Concrete Strength Using Support Vector Machines

2013 ◽  
Vol 438-439 ◽  
pp. 170-173 ◽  
Author(s):  
Hai Ying Yang ◽  
Yi Feng Dong
Keyword(s):  
Compressive Strength ◽  
Support Vector Machines ◽  
Concrete Strength ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Vector Machines ◽  
Compressive Strength Of Concrete

Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

Sign in / Sign up

Export Citation Format

Share Document