Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Generating Neural Networks Through the Induction of Threshold Logic Unit Trees (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Multivariate Versus Univariate Decision Trees
Multivariate Versus Univariate Decision Trees
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Machine Learning
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This paper investigates the generation of neural networks through the induction of binary trees of threshold logic units (TLUs). Initially, we describe the framework for our tree construction algorithm and show how it helps to bridge the gap between pure connectionist (neural network) and symbolic (decision tree) paradigms. We also show how the trees of threshold units that we induce can be transformed into an isomorphic neural network topology. Several methods for learning the linear discriminant functions at each node of the tree structure are examined and shown to produce accuracy results that are comparable to classical information theoretic methods for constructing decision trees (which use single feature tests at each node), but produce trees that are smaller and thus easier to understand. Moreover, our results also show that it is possible to simultaneously learn both the topology and weight settings of a neural network simply using the training data set that we are initially given.