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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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Morik, K.:

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

1989, ISBN: 354050768X

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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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Morik, K.:

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

1989, ISBN: 354050768X

Livro de bolso

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

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Knowledge Representation and Organization in Machine Learning: International Workshop - Papers (Lect - Livro de bolso

1989, ISBN: 354050768X

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Knowledge Representation and Organization in Machine Learning
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Número 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.

Dados detalhados do livro - Knowledge Representation and Organization in Machine Learning


EAN (ISBN-13): 9783540507680
ISBN (ISBN-10): 354050768X
Livro de capa dura
Livro de bolso
Ano de publicação: 1989
Editor/Editora: Springer Berlin Heidelberg
319 Páginas
Peso: 0,515 kg
Língua: eng/Englisch

Livro na base de dados desde 2007-05-31T21:00:49+01:00 (Lisbon)
Página de detalhes modificada pela última vez em 2024-02-12T12:09:44+00:00 (Lisbon)
Número ISBN/EAN: 354050768X

Número ISBN - Ortografia alternativa:
3-540-50768-X, 978-3-540-50768-0
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: berlin, morik
Título do livro: 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

Dados da editora

Autor: Katharina Morik
Título: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Knowledge Representation and Organization in Machine Learning
Editora: Springer; Springer Berlin
322 Páginas
Ano de publicação: 1989-01-25
Berlin; Heidelberg; DE
Língua: Inglês
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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