Communications of the ACM
Computational limitations on learning from examples
Journal of the ACM (JACM)
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Feature Transformation and Subset Selection
IEEE Intelligent Systems
Machine Learning
Machine Learning
An Efficient Algorithm for Computing Kernel Function Defined with Anti-unification
Inductive Logic Programming
Kernel Functions Based on Derivation
New Frontiers in Applied Data Mining
Online Rule Learning via Weighted Model Counting
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Efficiency versus convergence of Boolean kernels for on-line learning algorithms
Journal of Artificial Intelligence Research
Serving Comparative Shopping Links Non-invasively
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
ACIK: association classifier based on itemset kernel
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Learning action effects in partially observable domains
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
DRC-BK: mining classification rules by using Boolean kernels
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Web Intelligence and Agent Systems
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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 derived from given basic attributes. In addition, it is expected that SVMs' well-founded capacity control alleviates overfitting. In fact, an empirical study on learning of randomly generated Boolean functions shows that the resulting algorithm outperforms C4.5. Furthermore, in comparison with SVMs employing the Gaussian kernel, it is shown that DNF kernel produces accuracy comparable to best adjusted Gaussian kernels.