Introduction to support vector learning
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
AI Game Programming Wisdom
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machines for interval discriminant analysis
Neurocomputing
Ameva: An autonomous discretization algorithm
Expert Systems with Applications: An International Journal
Multi-classification with tri-class support vector machines: a review
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Mixing linear SVMs for nonlinear classification
IEEE Transactions on Neural Networks
Support vector machines for classification of input vectors with different metrics
Computers & Mathematics with Applications
Online motion recognition using an accelerometer in a mobile device
Expert Systems with Applications: An International Journal
A study on output normalization in multiclass SVMs
Pattern Recognition Letters
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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Support vector machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separable case, all the approaches can also be formulated as a unique dual optimization problem, however, their solutions are not equivalent. Discussions and remarks in the article point to an in-depth comparison between SVM formulations and associated parameters.