Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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Sivu 10
Suppose the training set consisted of N1 patterns belonging to category 1 and N2
patterns belonging to category 2 . Reasonable estimates for X , and X , might
then be the respective sample means ( centers of gravity ) of the patterns in each
 ...
Sivu 75
The subset Yi contains all training patterns in y belonging to category i . We
desire to train the linear machine by adjusting its weight vectors so that it
responds correctly to every pattern in Y . A response to a pattern in category i is
correct only if ...
Sivu 121
Suppose the modes for the various categories , as established by a training
procedure , are given by the points P ; ( ) for i = 1 , . . . , R and j = 1 , . . . , Li . That
is , there are L , typical patterns belonging to category 1 , L2 belonging to
category 2 ...

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Preface vii
1
3
43
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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 specific Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero

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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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QR code for Learning Machines
OtsikkoLearning Machines: Foundations of Trainable Pattern-classifying Systems
McGraw-Hill series in systems science
KirjoittajaNils J. Nilsson
Painoskuvitettu
KustantajaMcGraw-Hill, 1965
ISBN0070465703, 9780070465701
Pituus137 sivua
  
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