Extraction And Generalization of Expert Advice
Extraction And Generalization of Expert Advice
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In our eReader you can find the full English version of the book. Read Extraction And Generalization of Expert Advice Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book Extraction And Generalization of Expert Advice
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X Tip) (type "fli T, i '02 T22 ... 'a„ t. P) • • • (type "ai T„i "a, T„2 ... \ T„p) of negative objects of the class, i. E. , those that are not members of the class, derive a description of the class of objects. Typically, a class description is a predicate which depends upon the values of one or more attributes, and evaluates to "true" on all the positive instances, and "false" on all the negative instances.
In the above description of the generalization problem, the set of negative instances... may be empty, as it is possible to attempt generalization without the use of negative instances, as in [E)eJong83], who used large -61 amounts of domain knowledge to formulate accurate generalizations. However, the difficulty of this approach was recognized by [Winston7S], who used negative examples to closely delimit the set of positive examples, thus preventing overgeneralization. The need for negative examples to prevent overgeneralization is also recognized and explained in [Laird84].
What to read after Extraction And Generalization of Expert Advice?
You can find similar books in the "Read Also" column, or choose other free books by Paul Benjamin to read onlineMoreLess
To quickly assess the difficulty of the text, read a short excerpt:
X Tip) (type "fli T, i '02 T22 ... 'a„ t. P) • • • (type "ai T„i "a, T„2 ... \ T„p) of negative objects of the class, i. E. , those that are not members of the class, derive a description of the class of objects. Typically, a class description is a predicate which depends upon the values of one or more attributes, and evaluates to "true" on all the positive instances, and "false" on all the negative instances.
In the above description of the generalization problem, the set of negative instances... may be empty, as it is possible to attempt generalization without the use of negative instances, as in [E)eJong83], who used large -61 amounts of domain knowledge to formulate accurate generalizations. However, the difficulty of this approach was recognized by [Winston7S], who used negative examples to closely delimit the set of positive examples, thus preventing overgeneralization. The need for negative examples to prevent overgeneralization is also recognized and explained in [Laird84].
What to read after Extraction And Generalization of Expert Advice?
You can find similar books in the "Read Also" column, or choose other free books by Paul Benjamin to read onlineMoreLess
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