Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 504
... units . The weights connected to the output units can be adjusted in order to reduce those errors . We can then use the error estimates of the output units to derive error estimates for the units in the hidden layers . Finally , errors ...
... units . The weights connected to the output units can be adjusted in order to reduce those errors . We can then use the error estimates of the output units to derive error estimates for the units in the hidden layers . Finally , errors ...
Página 513
... 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 , ... , Xn ) ...
... 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 , ... , Xn ) ...
Página 518
Elaine Rich. State Units Plan Units Hidden Units Output Units Figure 18.22 : A Jordan Network of muscles ) , but we need more than a single ... Units Articulatory Units Hidden Plan Units Units Target Units 518 CHAPTER 18. CONNECTIONIST ...
Elaine Rich. State Units Plan Units Hidden Units Output Units Figure 18.22 : A Jordan Network of muscles ) , but we need more than a single ... Units Articulatory Units Hidden Plan Units Units Target Units 518 CHAPTER 18. CONNECTIONIST ...
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