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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / som_supervised </title></head><body bgcolor=#f0f0f0><table border=0 width="100%" cellpadding=0 cellspacing=0><tr><td valign=baseline><font size=+2>SOM Toolbox</font></td><td valign=baseline align=center><a href="somtoolbox.html">Online documentation</td><td valign=baseline align=right><a href="http://www.cis.hut.fi/projects/somtoolbox/" target="_top">http://www.cis.hut.fi/projects/somtoolbox/</a></td></tr></table><hr><H1> som_supervised </H1><H3> Purpose </H3><PRE> Creates, initializes and trains a supervised SOM by taking the class-identity into account.</PRE><H3> Syntax </H3><UL><PRE> sMap = som_supervised(sData); sMap = som_supervised(...,'argID',value,...) sMap = som_make(...,value,...);</PRE></UL><H3> Description </H3><PRE> Creates, initializes and trains a supervised SOM. It constructs the training data by adding 1-of-N -coded matrix to the original data based on the class information in the .labels field. The dimension of vectors after the process is (the old dimension + number of different classes). In each vector, one of the new components has value '1' (this depends on the class of the vector), and others '0'. Calls SOM_MAKE to construct the map. Then the class of each map unit is determined by taking maximum over these added components, and a label is give accordingly. Finally, the extra components (the 1-of-N -coded ones) are removed.</PRE><H3> References </H3><PRE> Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, Berlin, 1995, pp. 160-161. Kohonen, T., M鋕ivasara, K., Saram鋕i, T., "Phonetic Maps - Insightful Representation of Phonological Features For Speech Recognition", In proceedings of International Conference on Pattern Recognition (ICPR), Montreal, Canada, 1984, pp. 182-185.</PRE><H3> Required input arguments </H3><PRE> sData The data to use in the training. (struct) A data struct. '.comp_names' as well as '.name' is copied to the map. The class information is taken from the first column of '.labels' field.</PRE><H3> Optional input arguments </H3><PRE> argID (string) Argument identifier string (see below). value (varies) Value for the argument (see below). The optional arguments can be given as 'argID',value -pairs. If an argument is given value multiple times, the last one is used. Here are the argument IDs and corresponding values: 'munits' (scalar) the preferred number of map units - this may change a bit, depending on the properties of the data 'msize' (vector) map grid size 'mask' (vector) BMU search mask, size dim x 1 'name' (string) map name 'comp_names' (string array / cellstr) component names, size dim x 1 'tracking' (scalar) how much to report, default = 1. This parameter is also passed to the training functions. The following values are unambiguous and can therefore be given without the preceeding argument ID: 'algorithm' (string) training algorithm: 'seq' or 'batch' (default) 'mapsize' (string) do you want a 'small', 'normal' or 'big' map Any explicit settings of munits or msize (or topol) override this. 'topol' (struct) topology struct 'som_topol','sTopol' = 'topol' 'lattice' (string) map lattice, 'hexa' or 'rect' 'shape' (string) map shape, 'sheet', 'cyl' or 'toroid' 'neigh' (string) neighborhood function, 'gaussian', 'cutgauss', 'ep' or 'bubble'</PRE><H3> Output arguments </H3><PRE> sMap (struct) SOM -map struct</PRE><H3> Examples </H3><PRE> To simply train a map with default parameters: sMap = som_supervised(sData); With the optional arguments, the initialization and training can be influenced. To change map size, use 'msize', 'munits' or 'mapsize' arguments: sMap = som_supervised(D,'mapsize','big'); or sMap = som_supervised(D,'big'); sMap = som_supervised(D,'munits', 100); sMap = som_supervised(D,'msize', [20 10]); Argument 'algorithm' can be used to switch between 'seq' and 'batch' algorithms. 'batch' is the default, so to use 'seq' algorithm: sMap = som_supervised(D,'algorithm','seq'); or sMap = som_supervised(D,'seq'); The 'tracking' argument can be used to control the amout of reporting during training. The argument is used in this function, and it is passed to the training functions. To make the function work silently set it to 0. sMap = som_supervised(D,'tracking',0); </PRE><H3> See also </H3><TABLE NOBORDER WIDTH=80%><TR><TD><a href="som_make.html"><B>som_make</B></a><TD> Create, initialize and train Self-Organizing map.<TR><TD><a href="som_autolabel.html"><B>som_autolabel</B></a><TD> Label SOM/data set based on another SOM/data set.</TABLE><p><hr><br><br><!-- Last updated: May 30 2002 --></body></html>⌨️ 快捷键说明
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