The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on 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)
Implementation Issues of an Incremental and Decremental SVM
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning to Trade with Incremental Support Vector Regression Experts
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Incremental Kernel Machines for Protein Remote Homology Detection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Incremental and decremental learning for linear support vector machines
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
An effective incremental algorithm for ν-support vector machine
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Online support vector regression for system identification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Accurate on-line ν-support vector learning
Neural Networks
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This paper describes an on-line method for building 驴-insensitive support vector machines for regression as described in [12]. The method is an extension of the method developed by [1] for building incremental support vector machines for classification. Machines obtained by using this approach are equivalent to the ones obtained by applying exact methods like quadratic programming, but they are obtained more quickly and allow the incremental addition of new points, removal of existing points and update of target values for existing data. This development opens the application of SVM regression to areas such as on-line prediction of temporal series or generalization of value functions in reinforcement learning.