Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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Sivu 26
Since each of these discriminant functions gi ( X ) is a piecewise linear function of the components of X we shall call them piecewise linear discriminant functions . * Any machine employing piecewise linear discriminant functions will ...
Sivu 112
2-7 , a layered machine is a piecewise linear machine . A layered machine with P TLUs in the first layer has a total of 2P linear subsidiary discriminant functions ; these are divided into two classes ( corresponding to category 1 and ...
Sivu 134
... 72 , 75 disadvantages of , 118 graphical example of , 71 for linear machines , 75 numerical example of , 72 for 6 machines , 76 for piecewise linear machines , 116 for TLUs , 69 Estimation of modes , 123 Euclidean distance , 16 from ...

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SOME NONPARAMETRIC TRAINING METHODS
65
95
95
INDEX
133
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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 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

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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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