Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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Sivu 101
weight vectors which are adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ...
Sivu 103
are adjusted as shown since they are the closest to the Y , pattern hyperplane ( they make the two least - negative ... we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are made .
Sivu 117
What is needed , then , to train PWL machines is a method of adjusting weight vectors and a method for transferring ... At each pattern presentation the machine may be adjusted by changing weight vectors , by transferring weight vectors ...

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SOME NONPARAMETRIC TRAINING METHODS
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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 classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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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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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