Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 469
... version spaces have several deficiencies . One is the large space requirements of the exhaustive , breadth- first search mentioned above . Another is that inconsistent data , also called noise , can cause the candidate elimination ...
... version spaces have several deficiencies . One is the large space requirements of the exhaustive , breadth- first search mentioned above . Another is that inconsistent data , also called noise , can cause the candidate elimination ...
Página 485
Elaine Rich. 2. Implement the candidate elimination algorithm for version spaces . Choose a concept space with several features ( for example , the space of books , computers , animals , etc. ) Pick a concept and demonstrate learning by ...
Elaine Rich. 2. Implement the candidate elimination algorithm for version spaces . Choose a concept space with several features ( for example , the space of books , computers , animals , etc. ) Pick a concept and demonstrate learning by ...
Página 498
... space search in symbolic AI . Gradient descent is a learning . strategy , analogous to techniques such as version spaces . In both symbolic and connec- tionist AI , learning is viewed as a type of problem solving , and this is why ...
... space search in symbolic AI . Gradient descent is a learning . strategy , analogous to techniques such as version spaces . In both symbolic and connec- tionist AI , learning is viewed as a type of problem solving , and this is why ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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 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