Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!uwm.edu!ogicse!milton!nealiphc
From: nealiphc@milton.u.washington.edu (Phillip Neal)
Newsgroups: comp.ai.neural-nets
Subject: generalization in NN's
Keywords: ldf generalization
Message-ID: <1991Apr2.205240.24668@milton.u.washington.edu>
Date: 2 Apr 91 20:52:40 GMT
Organization: University of Washington
Lines: 30

I have a problem with the ability of a neural net to generalize.
I have 600 observations of a 6 predictor variable input vector
to classify these observations into 1 of 4 groups.

I break the data into a 400 observation training set and
a 200 observation test set.

When I use a simple linear discriminant function with seperate
covariance matrices and compare that against a NN with 6 input,
12 hidden and 4 output nodes. Here's what I get for correct
classification rates:

			LDF	NN
train			48.5	59.0
test			42.0	37.0

And no matter how long I let the NN run, and no matter what
number of hidden layer nodes, I always get about the same 
results.

So, what's the deal ? Is my sample size too small ? Are there
any good papers that cover this kind of problem ?

I know I am violating the rule of thumb to have 10 times more
training data than nodes in the net. But hey, data is expensive.

Thanks,

Phil Neal
phil@iris.iphc.washington.edu direct to my workstation