Approximate convex hulls family for one-class classification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
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In this letter, a two-layer neural network is proposed for computation of an approximate convex hull of a set of given points in 3-D or a set of spheres of different sizes. The algorithm is designed based on an elegant concept-shrinking of a spherical rubber balloon surrounding the set of objects in 3-D. Logically, a set of neurons is orderly placed on a spherical mesh i.e., on a rubber balloon surrounding the objects. Each neuron has a parameter vector associated with its current position. The resultant force of attraction between a neuron and each of the given points/objects, determines the direction of a movement of the neuron lying on the rubber balloon. As the network evolves, the neurons (parameter vectors) approximate the convex hull more and more accurately