Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating Prior Knowledge into SVM for Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Simpler knowledge-based support vector machines
ICML '06 Proceedings of the 23rd international conference on Machine learning
Reducing SVR Support Vectors by Using Backward Deletion
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
IEEE Transactions on Neural Networks
Nonlinear Knowledge-Based Classification
IEEE Transactions on Neural Networks
Support vector regression with a priori knowledge used in order execution strategies based on VWAP
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
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In this article, we extend the idea of a priori knowledge in the form of detractor points presented recently for Support Vector Classification. We show that detractor points can belong to the new type of support vectors - training samples which lie outside a margin bounded region. We present the new application for a priori knowledge from detractor points - improving generalization performance of Support Vector Classification while reducing a complexity of a model by removing a bunch of support vectors. The experiments show that indeed the new type of a priori knowledge improves generalization performance of reduced models. The tests were performed on selected classification data sets, and on stock price data from public domain repositories.