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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... weighted sum of its inputs and sending the output 1 if the sum is greater than some adjustable threshold value ( otherwise it sends 0 ) . Figure 18.5 shows the device . Notice that in a perceptron , unlike a Hopfield network ...
... weighted sum of its inputs and sending the output 1 if the sum is greater than some adjustable threshold value ( otherwise it sends 0 ) . Figure 18.5 shows the device . Notice that in a perceptron , unlike a Hopfield network ...
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... weighted inputs , but unlike the perceptron , it produces a real value between 0 and 1 as output , based on a sigmoid ( or S - shaped ) function , which is continuous and differentiable , as required by the backpropagation algorithm ...
... weighted inputs , but unlike the perceptron , it produces a real value between 0 and 1 as output , based on a sigmoid ( or S - shaped ) function , which is continuous and differentiable , as required by the backpropagation algorithm ...
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... weighted sum of its inputs.10 3. Let the output units fight until only one is active.'1 4. Adjust the weights on the input lines that lead to the single active output unit : Aw ; = n xj - n nw ; for all j = 1 , ... , n m where wj Xj is ...
... weighted sum of its inputs.10 3. Let the output units fight until only one is active.'1 4. Adjust the weights on the input lines that lead to the single active output unit : Aw ; = n xj - n nw ; for all j = 1 , ... , n m where wj Xj is ...
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