A fast linear separability test by projection of positive points on subspaces
Proceedings of the 24th international conference on Machine learning
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
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ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Estimation of classification complexity
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A novel and efficient method for testing non linear separability
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Expert Systems with Applications: An International Journal
Linear separability and classification complexity
Expert Systems with Applications: An International Journal
Linearly and quadratically separable classifiers using adaptive approach
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Integrated Fisher linear discriminants: An empirical study
Pattern Recognition
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The notion of linear separability is used widely in machine learning research. Learning algorithms that use this concept to learn include neural networks (single layer perceptron and recursive deterministic perceptron), and kernel machines (support vector machines). This paper presents an overview of several of the methods for testing linear separability between two classes. The methods are divided into four groups: Those based on linear programming, those based on computational geometry, one based on neural networks, and one based on quadratic programming. The Fisher linear discriminant method is also presented. A section on the quantification of the complexity of classification problems is included.