Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... Chapter 3G3 . PLANETABLE OPERATION Instructions in the basic techniques of planetable surveys , including the adjustment and maintenance of equipment . Part 3H . Chapter 3H1 . FIELD MAPPING AND COMPLETION SURVEYS Instructions for ...
... Chapter 3G3 . PLANETABLE OPERATION Instructions in the basic techniques of planetable surveys , including the adjustment and maintenance of equipment . Part 3H . Chapter 3H1 . FIELD MAPPING AND COMPLETION SURVEYS Instructions for ...
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... Chapter Second Place - Shawano FFA Chapter 102 chapters participated in the District , Sectional , and State contests . II . NATIONAL CHAPTER AWARDS This award is granted to chapters on the basis of activities in which the membership as ...
... Chapter Second Place - Shawano FFA Chapter 102 chapters participated in the District , Sectional , and State contests . II . NATIONAL CHAPTER AWARDS This award is granted to chapters on the basis of activities in which the membership as ...
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... Chapter under the jurisdiction of the General Grand Chap ter , and no Chapter shall be deemed legal without such Dispensation or Charter ; and Masonic communication , both public and private , is hereby interdicted and forbidden between ...
... Chapter under the jurisdiction of the General Grand Chap ter , and no Chapter shall be deemed legal without such Dispensation or Charter ; and Masonic communication , both public and private , is hereby interdicted and forbidden between ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 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 step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |