By Alder M.
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The trouble is, it requires the pattern recognising human to choose, for each geometrical object, some measurement process specific to the objects to be recognised. This is currently how things are done; when somebody writes a program to recognise chinese characters, he sits and thinks for a while about how to make some measurements on them so as to give some resulting point in or if not a point in for each character, , some other kind of representation. html (1 of 2) [12/12/2000 4:06:16 AM] Structured Patterns interested in classifying, he then tries to automate the process of producing the point or other symbolic representation describing the original object, and then he sets about writing a program to classify the representations.
It is typified by CART, and it works roughly as follows. Suppose we want to tell the gals from the guys again. We take the two dimensional weight and height representation for illustration. We first see how to cut the space up into two sections by working out the best place to put a hyperplane (line) in the space so as to get the largest fraction of points correctly discriminated. This is just like the MLP with a single unit so far. Then we look at the points that are wrong, and try to fix them up by further subdivision of the space.
We may have a mix of geometric and symbolic representations, and so on. My reason for not expanding on this area is because I am not happy with either the range of possible representation systems, or the segmentation process. I think there are better ways to do it, and I shall indicate them later in this book. Next: Clustering: supervised v unsupervised Up: Decisions, decisions.. html [12/12/2000 4:04:22 AM] Clustering: supervised v unsupervised learning Next: Dynamic Patterns Up: Basic Concepts Previous: CART et al Clustering: supervised v unsupervised learning The reflective reader will, perhaps, have been turning to the not so silly question of how he or she tells men from women.
An Introduction to Pattern Recognition by Alder M.