Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 494
... Perceptron with Many Inputs and Many Outputs is this : Whatever a perceptron can compute , it can learn to compute ! We demonstrate this in a moment . At the time perceptrons were invented , many people speculated that intelligent ...
... Perceptron with Many Inputs and Many Outputs is this : Whatever a perceptron can compute , it can learn to compute ! We demonstrate this in a moment . At the time perceptrons were invented , many people speculated that intelligent ...
Página 498
... perceptron incorrectly fails to fire , but add vector - if is an input for which the perceptron incorrectly fires . Multiply the sum by a scale factor n . 6. Modify the weights ( wo , w .... , w ) by adding the elements of the vector S ...
... perceptron incorrectly fails to fire , but add vector - if is an input for which the perceptron incorrectly fires . Multiply the sum by a scale factor n . 6. Modify the weights ( wo , w .... , w ) by adding the elements of the vector S ...
Página 500
... Perceptron That Solves the XOR Problem The perceptron ... has many features that attract attention : its linearity , its intriguing learning theorem ... there is no reason to suppose that any of these virtues carry over to the many ...
... Perceptron That Solves the XOR Problem The perceptron ... has many features that attract attention : its linearity , its intriguing learning theorem ... there is no reason to suppose that any of these virtues carry over to the many ...
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