Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 10
Sivu 32
... partitioned by a ( d1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d 2 as an example . Figure 2.9 ...
... partitioned by a ( d1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d 2 as an example . Figure 2.9 ...
Sivu 34
... partitions X ' and suppose that H ; can be made to pass through XN without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to XN , still without alter- ing the partition of X ...
... partitions X ' and suppose that H ; can be made to pass through XN without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to XN , still without alter- ing the partition of X ...
Sivu 106
... partition we mean that the hyperplanes divide O TLU 1 TLU 2 O O TLU 3 TLU 3 Origin TLU 2 ( a ) Pattern space TLU 1 ... partitioning the sets X1 and X2 arises because corresponding to each nonempty cell in the pat- tern space is a vertex ...
... partition we mean that the hyperplanes divide O TLU 1 TLU 2 O O TLU 3 TLU 3 Origin TLU 2 ( a ) Pattern space TLU 1 ... partitioning the sets X1 and X2 arises because corresponding to each nonempty cell in the pat- tern space is a vertex ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
5 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |