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ALT

2001

Springer

2001

Springer

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...

Related Content

Added |
15 Mar 2010 |

Updated |
15 Mar 2010 |

Type |
Conference |

Year |
2001 |

Where |
ALT |

Authors |
Ken Sadohara |

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