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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual demglm1
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> demglm1
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Demonstrate simple classification using a generalized linear model.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 demglm1</PRE>
wolffd@0 20
wolffd@0 21
wolffd@0 22 <p><h2>
wolffd@0 23 Description
wolffd@0 24 </h2>
wolffd@0 25
wolffd@0 26 The problem consists of a two dimensional input
wolffd@0 27 matrix <CODE>data</CODE> and a vector of classifications <CODE>t</CODE>. The data is
wolffd@0 28 generated from two Gaussian clusters, and a generalized linear model
wolffd@0 29 with logistic output is trained using iterative reweighted least squares.
wolffd@0 30 A plot of the data together with the 0.1, 0.5 and 0.9 contour lines
wolffd@0 31 of the conditional probability is generated.
wolffd@0 32
wolffd@0 33 <p><h2>
wolffd@0 34 See Also
wolffd@0 35 </h2>
wolffd@0 36 <CODE><a href="demglm2.htm">demglm2</a></CODE>, <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr>
wolffd@0 37 <b>Pages:</b>
wolffd@0 38 <a href="index.htm">Index</a>
wolffd@0 39 <hr>
wolffd@0 40 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 41
wolffd@0 42
wolffd@0 43 </body>
wolffd@0 44 </html>