Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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Within the framework provided by this approach , most of the previous and
present work in the field is interpreted as attempts either to understand the
properties of various discriminant functions or to find methods for their selection
or ...
Sivu 11
We examine the properties of some in detail , and present block diagrams to
suggest the manner in which they might be employed . Chapter 3 will investigate
decision - theoretic parametric training methods . The mathematical foundation ...
Sivu 103
5 Transformation properties of layered machines We have seen in Secs . 6 . 2 to
6 . 4 that the concept of the first - layer TLUS as voters in a “ committee ” is a
productive representation for two - layer machines . Another representation , to
be ...

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Preface vii
1
SOME NONPARAMETRIC TRAINING METHODS
65
TRAINING THEOREMS
79
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Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
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Yleiset termit ja lausekkeet

adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks 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 selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero

Tietoja kirjailijasta (1965)

Nils John Nilsson was born in Saginaw, Michigan on February 6, 1933. He received a bachelor's degree and a Ph.D. in electrical engineering from Stanford University. He joined the Air Force and served three years at the Rome Air Development Center before joining the Stanford Research Institute in 1961. He helped develop the first general-purpose robot and was a co-inventor of algorithms that made it possible for the machine to move about efficiently and perform simple tasks. He later worked on the "Computer-based Consultant," which focused on natural language understanding. He wrote several books including Learning Machines: Foundations of Trainable Pattern-Classifying Systems and The Quest for Artificial Intelligence: A History of Ideas and Achievements. He died on April 21, 2019 at the age of 86.

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OtsikkoLearning Machines: Foundations of Trainable Pattern-classifying Systems
McGraw-Hill series in systems science
KirjoittajaNils J. Nilsson
Painoskuvitettu
KustantajaMcGraw-Hill, 1965
Pituus137 sivua
  
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