595129781

Knowledge representation and organization in machine learning : [Workshop on Knowledge Representation and Organization in Machine Learning held in 1987 in Schloß Eringerfeld (Germany)] / K. Morik (ed.)

Workshop on Knowledge Representation and Organization in Machine Learning ; (Eringerfeld) : 1987.10.04-07

Englisch

Berlin [u.a.] : Springer, 1989

Online-Ressource (XIII, 319 S.)

Konferenzschrift (1987, Eringerfeld)

Literaturangaben

Druckausg.: Morik, Katharina *1956-*: Knowledge representation and organization in machine learning. - Berlin : Springer, 1989. - XIII, 319 S

978-3-540-46081-7

Weitere Ausgaben: 3-540-50768-X (Druckausgabe), 0-387-50768-X (Druckausgabe), 978-3-540-50768-0 (Druckausgabe)

DOI: 10.1007/BFb0017213

*Maschinelles Lernen / Wissensrepräsentation

*Maschinelles Lernen / Wissensorganisation

*Computer science ; Artificial intelligence ; Machine learning ; Knowledge representation (Information theory)

*Computer Science ; Artificial Intelligence (incl. Robotics)

Klassifikation der Library of Congress: Q325

Dewey Dezimal-Klassifikation: 006.3/1;

Regensburger Verbund-Klassifikation: ST 285: SQ-SU Informatik / ST Monografien / ST 230-ST 285 Software und -entwicklung / Computer supported cooperative work (CSCW), Groupware

Regensburger Verbund-Klassifikation: ST 304: SQ-SU Informatik / ST Monografien / ST 300-ST 308 Künstliche Intelligenz / Automatisches Programmieren, Deduction and theorem proving, Wissensrepräsentation

Regensburger Verbund-Klassifikation: SS 4800: SQ-SU Informatik / SS Enzyklopädien und Handbücher. Kongressberichte Schriftenreihe. Tafeln und Formelsammlungen / SS 4000-SS 5999 Schriftenreihen (indiv. Sign.) / Lecture notes in computer science

Mathematics Subject Classification: *68-06

Mathematics Subject Classification: 68T05

Mathematics Subject Classification: 68T99

Verlagsinfo: 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.

 

Online-Ausg.

Berlin [u.a.] : Springer, 2006

Berlin [u.a.] : Springer

https://zbmath.org/?q=an:0696.68018 [Zentralblatt MATH]

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http://dx.doi.org/10.1007/BFb0017213 [E-books (Springer)]