LVQ學習矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.
This package implements a Kalman filter as described in the
paper "A Statistical Algorithm for Estimating Speed from Single Loop
Volume and Occupancy Measurements" by D. J. Dailey.
Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.
