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<p>Given two subsets S/sub 1/ and S/sub 2/ (not necessarily finite) of /spl Rfr//sup d/ separable by a Boolean combination of learning half-spaces, the authors consider the problem of (in the sense of Valiant) the separation function from a finite set of examples, i.e., they produce with a high probability a function close to the actual separating function. The authors' solution consists of a system of N perceptrons and a single consolidator which combines the outputs of the individual perceptrons; it is shown that an off-line version of this problem, where the examples are given in a batch, can be solved in time polynomial in the number of examples. The authors also provide an on-line learning algorithm that incrementally solves the problem by suitably training a system of N perceptrons much in the spirit of the classical perceptron learning algorithm.</p>
neural nets; learning (artificial intelligence); separations; Boolean combinations; learning half spaces; separation function; perceptrons; consolidator; online learning algorithm
C.W. Glover, N.S.V. Rao, E.M. Oblow, "Learning Separations by Boolean Combinations of Half-Spaces", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 765-768, July 1994, doi:10.1109/34.297960
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