Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 502
... backpropagation networks . The unit in a backpropagation network requires a slightly different activation function from the perceptron . Both functions are shown in Figure 18.16 . A backpropagation unit still sums up its weighted inputs ...
... backpropagation networks . The unit in a backpropagation network requires a slightly different activation function from the perceptron . Both functions are shown in Figure 18.16 . A backpropagation unit still sums up its weighted inputs ...
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 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
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
Derechos de autor | |
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Abbott agents algorithm answer apply approach ARMEMPTY assertions attributes axioms backpropagation backtracking backward belief best-first search breadth-first search Caesar called Chapter chess clauses complete concept conceptual dependency consider constraints contains contradiction corresponding define depth-first depth-first search described discussed domain fact frame function game tree goal grammar graph heuristic Horn clauses important inference inheritance input instance interpretation isa links John justification knowledge base knowledge representation labeled learning Marcus match minimax move MYCIN natural language node object ON(B operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG properties represent result robot rules script Section semantic semantic net sentence shown in Figure simple slot solution solve specific step structure Suppose syntactic task techniques theorem things tree truth maintenance system understanding variables version space