Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 504
... backpropagation network usually requires many epochs . Refer back to Figure 18.14 for the basic structure on which the following algorithm is based . Algorithm : Backpropagation Given : A set of input - output vector pairs . Compute : A ...
... backpropagation network usually requires many epochs . Refer back to Figure 18.14 for the basic structure on which the following algorithm is based . Algorithm : Backpropagation Given : A set of input - output vector pairs . Compute : A ...
Página 507
... backpropagation network , however , may slide down the error surface into a set of weights that does not solve the problem it is being trained on . If that set of weights is at a local minimum , the network will never reach the optimal ...
... backpropagation network , however , may slide down the error surface into a set of weights that does not solve the problem it is being trained on . If that set of weights is at a local minimum , the network will never reach the optimal ...
Página 508
... backpropagation . Performance on the test set ( examples that the network is not allowed to learn on ) also improves , although it is never quite as good as the training set . After a while , network performance reaches a plateau as the ...
... backpropagation . Performance on the test set ( examples that the network is not allowed to learn on ) also improves , although it is never quite as good as the training set . After a while , network performance reaches a plateau as the ...
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