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In den WarenkorbZustand: Sehr gut. Zustand: Sehr gut | Seiten: 380 | Sprache: Englisch | Produktart: Bücher.
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In den WarenkorbZustand: Acceptable. Used - Acceptable. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library with wear and barcode page may have been removed. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.
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Verlag: Sankt Augustin, infix, 1998
Sprache: Englisch
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In den WarenkorbSoftcover/Paperback. Zustand: Gut. 384 S. Good condition. Sprache: Englisch Gewicht in Gramm: 825.
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In den WarenkorbZustand: Gut. 384 S., graph. Darst. Handschriftlicher Besitzvermerk auf der Vorsatzseite. Innen neuwertig. Proceedings in artificial intelligence ; 7. Aus dem Inhalt: Invited Lectures, Sets, Fuzzy Sets and Rough Sets, Session 1: Fuzzy Control, Session 2: Neutral Networks for Classification, Session 3: Fuzzy Logic in Data Analysis, Session 4: Fuzzy-Neuro Systems, Session 5: Applications, Session6: Theory and Foundations of Fuzzy-Logic, Session 7: Theory and Foundations of Neural Networks, Poster Presentations. Innen tadellos erhalten. deu B12-01-05B|GH52|8477DB Sprache: Deutsch Gewicht in Gramm: 444 24 cm. Original Broschur. Sehr gut erhalten.
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Verlag: LAP LAMBERT Academic Publishing, 2010
ISBN 10: 384337144X ISBN 13: 9783843371445
Sprache: Englisch
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In den WarenkorbTaschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge 'buried' in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge 'encoded' in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.
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In den WarenkorbBuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge 'buried' in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge 'encoded' in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.