By Alder M.
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Extra info for An Introduction to Pattern Recognition
So if (for the data set of guys and gals) the nearest point to P is an X, then we conclude that P should be a man. If a rationale is needed, we could argue that the measurement process is intended to extract important properties of the objects, and if we come out with values for the readings which are close together, then the objects must be similar. And if they are similar in respect of the measurements we have made, they ought, in any reasonable universe, to be similar in respect of the category they belong to as well.
Then we try to classify the symbol strings. This might seem, to the naive, a bizarre approach, but it might sound more impressive if we spoke of vector quantisation and Hidden Markov Models. In this form, it is more or less a staple of speech recognition, and is coming into favour in other forms of trajectory analysis. The classification of trajectories, either in or in some discrete alphabet space, will also therefore preoccupy us at later stages. html (1 of 2) [12/12/2000 4:06:08 AM] Dynamic Patterns clean up signals have developed adaptive filters which have to learn properties of the signal as the signal is transmitted, and statisticians and physicists have studied ways to clean up dirty pictures.
It is clear that human beings don't have to learn by being told everything in this way; they can figure out things for themselves. It would be nice if our programs could do the same, if only in a small way. This can in fact be done, by methods which may perhaps mimic, abstractly, the central nervous system, and I shall describe them in later chapters under the heading of Syntactic Pattern Recognition. html (2 of 2) [12/12/2000 4:06:16 AM] Alternative Representations Next: Strings, propositions, predicates and Up: Basic Concepts Previous: Structured Patterns Alternative Representations In this slow and gentle walk through the basic ideas of Pattern Recognition, I have concentrated on representing objects by some kind of measuring process which translates them into points in a vector space.
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