Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 504
... input units . Unlike the perceptron learning algorithm of the last section , the backpropagation algorithm usually updates its weights incrementally , after seeing each input - output pair . After it has seen all the input - output ...
... input units . Unlike the perceptron learning algorithm of the last section , the backpropagation algorithm usually updates its weights incrementally , after seeing each input - output pair . After it has seen all the input - output ...
Página 511
... input data , the network is allowed to play with it to try to discover regularities and relationships between the different parts of the input . Learning is often made possible through some notion of which features in the input set are ...
... input data , the network is allowed to play with it to try to discover regularities and relationships between the different parts of the input . Learning is often made possible through some notion of which features in the input set are ...
Página 513
... input units directly connected to any number of output units . Produce : A set of weights such that the output units become active according to some natural division of the inputs . 1. Present an input vector , denoted ( X1 , X2 ...
... input units directly connected to any number of output units . Produce : A set of weights such that the output units become active according to some natural division of the inputs . 1. Present an input vector , denoted ( X1 , X2 ...
Contenido
Weak SlotandFiller Structures | 9 |
6 | 24 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
Otras 24 secciones no mostradas
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Términos y frases comunes
Abbott algorithm answer apply approach Artificial Intelligence assertions attributes axioms backpropagation backtracking backward backward reasoning belief best-first search breadth-first search Cabot Caesar Chapter clauses concept consider constraints contains contexts contradiction corresponding define depth-first depth-first search described discussed domain example explicitly fact given goal graph heuristic heuristic function Horn clauses important inference inheritance input instance interpretation justification knowledge base knowledge representation labeled learning logical assertions Marcus match move MYCIN node nonmonotonic reasoning object operators particular path perceptron possible preconditions predicate logic problem problem-solving procedure produce production system PROLOG propagation propositional logic question represent resolution result robot rules Section semantic semantic net sentence shown in Figure simple slot solution solve space specific statements step strategy structure Suppose suspect syntactic task techniques theorem things tree true truth maintenance system variables wff's