Computational geometry: an introduction
Computational geometry: an introduction
Machine Learning
The linear separability problem: some testing methods
IEEE Transactions on Neural Networks
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The notion of linear separability is widely used 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). Several algorithms for testing linear separability exist. Some of these methods are computationally intense. Also, several of them will converge if the classes are linearly separable, but will fail to converge otherwise. A fast and efficient test for non linear separability is proposed which can be used to pretest classification data sets for non linear separability thus avoiding expensive computations. This test is based on the convex hull separability method but does not require the computation of the convex hull.