Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas A revision of an established text for undergraduate and postgraduate Artificial Intelligence courses, this text incorporates the latest research and methods. |
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Página 502
... output is given by : output = 1 1+ e -sum sum Notice that if the sum is 0 , the output is 0.5 ( in contrast to the perceptron , where it must be either 0 or 1 ) . As the sum gets larger , the output approaches 1. As the sum gets smaller ...
... output is given by : output = 1 1+ e -sum sum Notice that if the sum is 0 , the output is 0.5 ( in contrast to the perceptron , where it must be either 0 or 1 ) . As the sum gets larger , the output approaches 1. As the sum gets smaller ...
Página 504
... output layer . During the backward pass , the network's actual output ( from the forward pass ) is compared with the target output and error estimates are computed for the output units . The weights connected to the output units can be ...
... output layer . During the backward pass , the network's actual output ( from the forward pass ) is compared with the target output and error estimates are computed for the output units . The weights connected to the output units can be ...
Página 513
... 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 ) . 2. Calculate the initial activation ...
... 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 ) . 2. Calculate the initial activation ...
Contenido
What Is Artificial Intelligence? | 3 |
5 | 24 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
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Términos y frases comunes
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 example fact 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 ON(C operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG 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