The nature of statistical learning theory
The nature of statistical learning theory
IEEE Intelligent Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Time series prediction using support vector machines: a survey
IEEE Computational Intelligence Magazine
Engineering Applications of Artificial Intelligence
Modeling partial customer churn: On the value of first product-category purchase sequences
Expert Systems with Applications: An International Journal
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Support vector machines (SVMs) are promising methods for the prediction of online auto insurance customer churning because SVMs use a risk minimization principal that consists of the empirical error and the regularized term predicting the switching probability of an insured to other auto insurance company. In addition, this study examines the feasibility of applying SVM in online insurance customer churning by comparing it with other methods such as artificial neural network (ANN) and logit model. This study proves that SVM provides a promising alternative to predict customer churning in autoinsurance service.