Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 504
... weights . The network adjusts its weights each time it sees an input - output pair . Each pair requires two stages : a forward pass and a backward pass . The forward pass involves presenting a sample input to the network and letting ...
... weights . The network adjusts its weights each time it sees an input - output pair . Each pair requires two stages : a forward pass and a backward pass . The forward pass involves presenting a sample input to the network and letting ...
Página 507
... weights is at a local minimum , the network will never reach the optimal set of weights . Thus , we have no analogue of the perceptron convergence theorem for backpropagation networks . There are several methods of overcoming the ...
... weights is at a local minimum , the network will never reach the optimal set of weights . Thus , we have no analogue of the perceptron convergence theorem for backpropagation networks . There are several methods of overcoming the ...
Página 513
... weights on the input lines that lead to the single active output unit : Xj Aw ; = n nw for all j = 1 , ... , n where m wj is the weight on the connection from input unit j to the active output unit , x ; is the value of the jth input ...
... weights on the input lines that lead to the single active output unit : Xj Aw ; = n nw for all j = 1 , ... , n where m wj is the weight on the connection from input unit j to the active output unit , x ; is the value of the jth input ...
Contenido
Weak SlotandFiller Structures | 9 |
6 | 24 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
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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