Acquiring and combining overlapping concepts
نویسندگان
چکیده
منابع مشابه
RUNNING HEAD: ACQUIRING CONTEXTUALIZED CONCEPTS Acquiring Contextualized Concepts: A Connectionist Approach
Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize o...
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Explanation-based generalization algorithms need to generalize the structure of their explanations. This is necessary in order to acquire concepts where a recursive or iterative process is implicitly represented in the explanation by a fixed number of applications. The fully-implemented BAGGER2 system generalizes explanation structures and produces recursive concepts when warranted. Otherwise t...
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Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize o...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1994
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993176