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| LEADER |
02035cam a22002534a 4500 |
| 001 |
27480 |
| 003 |
DLC |
| 005 |
20100413162359.0 |
| 008 |
090601s2010 paua b 001 0 eng |
| 020 |
# |
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|a 9781605668116 (ebook)
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| 040 |
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|a DLC
|c DLC
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| 050 |
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|a Q325.5
|b .N35 2010
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| 100 |
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|a Naidenova, Xenia,
|d 1940-
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| 245 |
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|a Machine learning methods for commonsense reasoning processes :
|b interactive models /
|c Xenia Naidenova.
|h [electronic resource]
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| 260 |
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|a Hershey, PA :
|b Information Science Reference,
|c 2010
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| 300 |
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|a 1 online resource (xiv, 410 p.)
|b ill. ;
|c 29 cm.
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| 504 |
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|a Includes bibliographical references (p. 400-401) and index.
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| 505 |
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|a Knowledge in the psychology of thinking and mathematics -- Logic-based reasoning in the framework of artiticial intelligence -- The coordination of commonsense reasoning operations -- The logical rules of commonsense reasoning -- The examples of human connonsense reasoning processes -- Machine learning (ML) as a diagnostic task -- The concept of good classification (diagnostic) test -- The duality of good diagnostic tests -- Towards an integrative model of deductive-inductive commonsense reasoning -- Towards a model of fuzzy commonsense reasoning -- Object-oriented technology for expert system generation -- Case technology for psycho-diagnostic system generation -- Commonsense reasoning in intelligent computer systems.
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| 520 |
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|a "The main purpose of this book is to demonstrate the possibility of transforming a large class of machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences are not separated one from another but moreover they are correlated and support one another"--Provided by publisher.
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| 650 |
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|a Machine learning.
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| 650 |
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|a Correlation (Statistics)
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| 650 |
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|a Recursive partitioning.
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-60566-810-9
|z View fulltext via EzAccess
|