Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 12
Miller11 illustrates a method for selecting a small number of " good " measurements from a larger pool of measurements . Block , Nilsson , and Duda describe a method for determining features of patterns . Some specific examples of ...
Miller11 illustrates a method for selecting a small number of " good " measurements from a larger pool of measurements . Block , Nilsson , and Duda describe a method for determining features of patterns . Some specific examples of ...
Sivu 30
That is , the WM parameters which determine a specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this ...
That is , the WM parameters which determine a specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this ...
Sivu 45
Using Eq . ( 3.1 ) , we could calculate Lx ( 2 ) for any specific X and for all possible values of i ( i = 1 , 1 .. , R ) . Suppose for some specific X , Lx ( i ) is a a minimum for i = 1o . That is , L ( 20 ) < Lx ( i ) for i = 1 , ...
Using Eq . ( 3.1 ) , we could calculate Lx ( 2 ) for any specific X and for all possible values of i ( i = 1 , 1 .. , R ) . Suppose for some specific X , Lx ( i ) is a a minimum for i = 1o . That is , L ( 20 ) < Lx ( i ) for i = 1 , ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |