An Adaptive Classification Scheme to Approximate Decision Boundaries Using Local Bayes Criteria - The "Melting Octree" Network

TitleAn Adaptive Classification Scheme to Approximate Decision Boundaries Using Local Bayes Criteria - The "Melting Octree" Network
Publication TypeTechnical Report
Year of Publication1992
AuthorsEncarnação, L. Miguel, & Gross M.. H.
Other Numbers752
Abstract

The following paper describes a new method to approximate the minimum error decision boundary for any supervised classification problem by means of a linear neural network consisting of simple neurons that use a local Bayes criterium and a next neighbor decision rule. The neurons can be interpreted as centroids in feature space or as a set of particles moving towards the classification boundary during training. In contrary to existing LVQ methods and RCE networks each neuron has a receptive field of an adjustable width e and the goal of the supervised training method is completely different. Furthermore, the network is able to grow in the sense of generating new entities in order to decrease the classification error after learning.For this purpose we initialize the network via a multidimensional octree representation of the training data set. The neurons generated during initialization only depend on the maximum number of data in a single octree cell. The learning method introduced ensures that all neurons move towards the class boundaries by checking the local Bayes criterium in their receptive field. For this process can also be interpreted as a melting away of the initial octree, we called the network "The Melting Octree" network.This report first describes the algorithms used for initialization, training as well as for growing of the net. The classification performance of the algorithm is then illustrated by some examples and compared with those of a Kohonen feature Map (LVQ) and of a backpropagated multilayered perceptron.Note: The charts are page 39 of the techreport. I stored them under TR-92-047.charts.ps.Z. They're not absolutely necessary for the report; just to complete it.

URLhttp://www.icsi.berkeley.edu/ftp/global/pub/techreports/1992/tr-92-047.pdf
Bibliographic Notes

ICSI Technical Report TR-92-047

Abbreviated Authors

L. M. Encarnacao and M. H. Gross

ICSI Publication Type

Technical Report