Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 463
... Example of the Concept Car set of possible descriptions and evolving that set as new examples and near misses are presented . As in the previous section , we need some sort of representation language for examples so that we can describe ...
... Example of the Concept Car set of possible descriptions and evolving that set as new examples and near misses are presented . As in the previous section , we need some sort of representation language for examples so that we can describe ...
Página 471
... example , testing the attribute color is useless if the color of a car does not help us to classify it correctly . Ideally , an attribute will separate training instances into subsets whose members share a common label ( e.g. , positive ...
... example , testing the attribute color is useless if the color of a car does not help us to classify it correctly . Ideally , an attribute will separate training instances into subsets whose members share a common label ( e.g. , positive ...
Página 472
... example x by explaining why x is an example of the target concept . The explanation is then generalized , and the system's performance is improved through the availability of this knowledge . Mitchell et al . [ 1986 ] and DeJong and ...
... example x by explaining why x is an example of the target concept . The explanation is then generalized , and the system's performance is improved through the availability of this knowledge . Mitchell et al . [ 1986 ] and DeJong and ...
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