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Knowledge Representation and Organization in Machine Learning. Lecture Notes in Artificial Intelligence 347 (Edited by J. Siekmann) - Morik, K.
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Knowledge Representation and Organization in Machine Learning. Lecture Notes in Artificial Intelligence 347 (Edited by J. Siekmann) - exemplaire signée

1989, ISBN: 354050768X

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Morik, K.:

Knowledge Representation and Organization in Machine Learning. Lecture Notes in Artificial Intelligence 347 (Edited by J. Siekmann) - exemplaire signée

1989, ISBN: 354050768X

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[EAN: 9783540507680], Gebraucht, sehr guter Zustand, [PU: Springer Berlin], 319 Seiten altersentsprechend gebrauchtes gutes Exemplar, Einband leicht berieben, Inhalt ist gut bis sehr gut … Plus…

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Knowledge Representation and Organization in Machine Learning: International Workshop - Papers (Lect - Livres de poche

1989, ISBN: 354050768X

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Détails sur le livre
Knowledge Representation and Organization in Machine Learning
Auteur:
Titre:
ISBN:

354050768X


Machine learning has become a rapidly growing field of Artificial Intelligence. Since the First International Workshop on Machine Learning in 1980, the number of scientists working in the field has been increasing steadily. This situation allows for specialization within the field. There are two types of specialization: on subfields or, orthogonal to them, on special subjects of interest. This book follows the thematic orientation. It contains research papers, each of which throws light upon the relation between knowledge representation, knowledge acquisition and machine learning from a different angle. Building up appropriate representations is considered to be the main concern of knowledge acquisition for knowledge-based systems throughout the book. Here machine learning is presented as a tool for building up such representations. But machine learning itself also states new representational problems. This book gives an easy-to-understand insight into a new field with its problems and the solutions it offers. Thus it will be of good use to both experts and newcomers to the subject.

Informations détaillées sur le livre - Knowledge Representation and Organization in Machine Learning


EAN (ISBN-13): 9783540507680
ISBN (ISBN-10): 354050768X
Version reliée
Livre de poche
Date de parution: 1989
Editeur: Springer Berlin Heidelberg
319 Pages
Poids: 0,515 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2007-05-31T22:00:49+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-02-12T13:09:44+01:00 (Zurich)
ISBN/EAN: 354050768X

ISBN - Autres types d'écriture:
3-540-50768-X, 978-3-540-50768-0
Autres types d'écriture et termes associés:
Auteur du livre: berlin, morik
Titre du livre: machine intelligence, not, organization science, computer organization, lecture notes artificial intelligence, international organization, the machine seen, paper knowledge, knowledge representation and organization machine learning, 347

Données de l'éditeur

Auteur: Katharina Morik
Titre: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Knowledge Representation and Organization in Machine Learning
Editeur: Springer; Springer Berlin
322 Pages
Date de parution: 1989-01-25
Berlin; Heidelberg; DE
Langue: Anglais
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XVIII, 322 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; artificial intelligence; expert system; inference engine; intelligence; knowledge base; knowledge representation; knowledge-based system; knowledge-based systems; learning; machine learning; ontology; Artificial Intelligence; EA

Explanation: A source of guidance for knowledge representation.- (Re)presentation issues in second generation expert systems.- Some aspects of learning and reorganization in an analogical representation.- A knowledge-intensive learning system for document retrieval.- Constructing expert systems as building mental models or toward a cognitive ontology for expert systems.- Sloppy modeling.- The central role of explanations in disciple.- An inference engine for representing multiple theories.- The acquisition of model-knowledge for a model-driven machine learning approach.- Using attribute dependencies for rule learning.- Learning disjunctive concepts.- The use of analogy in incremental SBL.- Knowledge base refinement using apprenticeship learning techniques.- Creating high level knowledge structures from simple elements.- Demand-driven concept formation.
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