NDLI: Generating neural networks through the induction of threshold logic unit trees

Content Provider	IEEE Xplore Digital Library
Author	Sahami, M.
Copyright Year	1995
Description	Author affiliation: Robotics Lab., Stanford Univ., CA, USA (Sahami, M.)
Abstract	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.<>
Starting Page	108
Ending Page	115
File Size	776341
Page Count	8
File Format	PDF
ISBN	0818671165
DOI	10.1109/INBS.1995.404272
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-05-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges Tree data structures Network topology Induction generators Neural networks Training data Binary trees Logic Decision trees Testing
Content Type	Text
Resource Type	Article

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Generating Neural Networks Through the Induction of Threshold Logic Unit Trees

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