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...

• Type: Conference
• Year: 2001
• Where: ALT
• Authors: Ken Sadohara
• Keywords: ALT 2001, Boolean Functions, Boolean Literals, Machine Learning, Monotone Dnf Kernel