We present a new, very fast and efficient learning algorithm for binary linear classification derived from an earlier neural model developed by one of the authors. The original method was based on the idea of describing the solution cone, ie. the convex region containing the separating vectors for a given set of patterns and then updating this region every time a new pattern is introduced. The drawback of that method was the high memory and computational costs required for computing and storing the edges that describe the cone. In the modification presented here we avoid these problems by obtaining just one solution vector inside the cone using an iterative rule, thus greatly simplifying and accelerating the process at the cost of very few misclassification errors. Even these errors can be corrected, to a large extend, using various techniques. Our method was tested on the real-world application of Named Entities Recognition obtaining results comparable to other state of the art classification methods.
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