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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) - ejemplar autografiado

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) - ejemplar autografiado

1989, ISBN: 354050768X

Pasta blanda

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

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Knowledge Representation and Organization in Machine Learning
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ISBN: 9783540507680

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Knowledge Representation and Organization in Machine Learning: International Workshop - Papers (Lect - Pasta blanda

1989, ISBN: 354050768X

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Detalles del libro
Knowledge Representation and Organization in Machine Learning
Autor:
Título:
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.

Detalles del libro - Knowledge Representation and Organization in Machine Learning


EAN (ISBN-13): 9783540507680
ISBN (ISBN-10): 354050768X
Tapa dura
Tapa blanda
Año de publicación: 1989
Editorial: Springer Berlin Heidelberg
319 Páginas
Peso: 0,515 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2007-05-31T15:00:49-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-02-12T06:09:44-06:00 (Mexico City)
ISBN/EAN: 354050768X

ISBN - escritura alterna:
3-540-50768-X, 978-3-540-50768-0
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: berlin, morik
Título del libro: 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

Datos del la editorial

Autor: Katharina Morik
Título: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Knowledge Representation and Organization in Machine Learning
Editorial: Springer; Springer Berlin
322 Páginas
Año de publicación: 1989-01-25
Berlin; Heidelberg; DE
Idioma: Inglés
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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