A Nearest Hyperrectangle Learning Method
Machine Learning
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Using neural networks for data mining
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On global, local, mixed and neighborhood kernels for support vector machines
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
IEEE Transactions on Neural Networks
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Interval-Valued neural multi-adjoint logic programs
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
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An hybrid SVM-symbolic architecture for classification tasks is proposed in this work The learning system relies on a support vector machine (SVM), meanwhile a rule extraction module translate the embedded knowledge in the trained SVM in the form of symbolic rules. The new representation is useful to understand the nature of the problem and its solution. Moreover, a rule insertion module in the hybrid architecture allows incorporate the available prior domain knowledge into the machine expressed in the form of symbolic rules. Thus, it is render possible the integration of SVMs with symbolic AI systems.