Learning of Boolean Functions Using Support Vector Machines

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Learning of Boolean Functions Using Support Vector Machines
This paper concerns the design of a Support Vector Machine (SVM) appropriate for the learning of Boolean functions. This is motivated by the need of a more sophisticated algorithm for classification in discrete attribute spaces. Classification in discrete attribute spaces is reduced to the problem of learning Boolean functions from examples of its input/output behavior. Since any Boolean function can be written in Disjunctive Normal Form (DNF), it can be represented as a weighted linear sum of all possible conjunctions of Boolean literals. This paper presents a particular kernel function called the DNF kernel which enables SVMs to efficiently learn such linear functions in the high-dimensional space whose coordinates correspond to all possible conjunctions. For a limited form of DNF consisting of positive Boolean literals, the monotone DNF kernel is also presented. SVMs employing these kernel functions can perform the learning in a high-dimensional feature space whose features are de...
Ken Sadohara
Added 15 Mar 2010
Updated 15 Mar 2010
Type Conference
Year 2001
Where ALT
Authors Ken Sadohara
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