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Kartoniert, 448 Seiten, 240mm x 160mm x 25mm, Sprache(n): eng One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involv… Mehr…
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2011, ISBN: 9781461294061
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2011, ISBN: 9781461294061
Editor: Mitchell, Tom M. Editor: Carbonell, Jaime G. Editor: Michalski, Ryszard S. Springer, Paperback, Auflage: Softcover reprint of the original 1st ed. 1986, 445 Seiten, Publiziert: 20… Mehr…
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2011, ISBN: 1461294061
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2011, ISBN: 9781461294061
Kartoniert, 448 Seiten, 240mm x 160mm x 25mm, Sprache(n): eng One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involv… Mehr…
2011, ISBN: 9781461294061
Springer, Taschenbuch, Auflage: Softcover reprint of the original 1st ed. 1986, 448 Seiten, Publiziert: 2011-10-14T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: biography, 1.38 kg, Info… Mehr…
2011
ISBN: 9781461294061
Editor: Mitchell, Tom M. Editor: Carbonell, Jaime G. Editor: Michalski, Ryszard S. Springer, Paperback, Auflage: Softcover reprint of the original 1st ed. 1986, 445 Seiten, Publiziert: 20… Mehr…
2011, ISBN: 1461294061
Softcover reprint of the original 1st ed. 1986 Kartoniert / Broschiert artificial intelligence; Case-Based Reasoning; circuit design; classification; heuristics; intelligence; logical r… Mehr…
ISBN: 9781461294061
One Of The Currently Most Active Research Areas Within Artificial Intelligence Is The Field Of Machi Libros>Informática>Informática práctica>Ordenadores. Conceptos. Elementos, Kno
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Detailangaben zum Buch - Machine Learning: A Guide to Current Research (The Springer International Series in Engineering and Computer Science, Band 12)
EAN (ISBN-13): 9781461294061
ISBN (ISBN-10): 1461294061
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2011
Herausgeber: Mitchell, Tom M. Carbonell, Jaime G. Michalski, Ryszard S. Springer
Buch in der Datenbank seit 2014-05-26T08:43:12+02:00 (Berlin)
Detailseite zuletzt geändert am 2024-05-17T06:11:29+02:00 (Berlin)
ISBN/EAN: 9781461294061
ISBN - alternative Schreibweisen:
1-4612-9406-1, 978-1-4612-9406-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: carbone, tom mitchell, michalski, carbonell
Titel des Buches: machine learning, research
Daten vom Verlag:
Autor/in: Tom M. Mitchell; Jaime G. Carbonell; Ryszard S. Michalski
Titel: The Springer International Series in Engineering and Computer Science; Machine Learning - A Guide to Current Research
Verlag: Springer; Springer US
429 Seiten
Erscheinungsjahr: 2011-10-14
New York; NY; US
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
267,49 € (DE)
274,99 € (AT)
295,00 CHF (CH)
POD
XVI, 429 p.
BC; Hardcover, Softcover / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; artificial intelligence; case-based reasoning; circuit design; classification; heuristics; intelligence; learning; logical reasoning; machine learning; robot; Artificial Intelligence; BB; EA
Judge: A Case-Based Reasoning System.- Changing Language While Learning Recursive Descriptions from Examples.- Learning by Disjunctive Spanning.- Transfer of Knowledge between Teaching and Learning Systems.- Some Approaches to Knowledge Acquisition.- Analogical Learning with Multiple Models.- The World Modelers Project: Objectives and Simulator Architecture.- The Acquisition of Procedural Knowledge through Inductive Learning.- Learning Static Evaluation Functions by Linear Regression.- Plan Invention and Plan Transformation.- A Brief Overview of Explanatory Schema Acquisition.- The EG Project: Recent Progress.- Learning Causal Relations.- Functional Properties and Concept Formation.- Explanation-Based Learning in Logic Circuit Design.- A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects.- Exploiting Functional Vocabularies to Learn Structural Descriptions.- Combining Numeric and Symbolic Learning Techniques.- Learning by Understanding Analogies.- Analogical Reasoning in the Context of Acquiring Problem Solving Expertise.- Planning and Learning in a Design Domain: The Problems Plan Interactions.- Inference of Incorrect Operators.- A Conceptual Framework for Concept Identification.- Neural Modeling as One Approach to Machine Learning.- Steps Toward Building a Dynamic Memory.- Learning by Composition.- Knowledge Acquisition: Investigations and General Principles.- Purpose-Directed Analogy: A Summary of Current Research.- Development of a Framework for Contextual Concept Learning.- On Safely Ignoring Hypotheses.- A Model of Acquiring Problem Solving Expertise.- Another Learning Problem: Symbolic Process Prediction.- Learning at LRI Orsay.- Coper: A Methodology for Learning Invariant Functional Descriptions.- Using Experience as a Guide for Problem Solving.- Heuristics as Invariants and its Application to Learning.- Components of Learning in a Reactive Environment.- The Development of Structures through Interaction.- Complex Learning Environments: Hierarchies and the use of Explanation.- Prediction and Control in an Active Environment.- Better Information Retrieval through Linguistic Sophistication.- Machine Learning Research in the Artificial Intelligence Laboratory at Illinois.- Overview of the Prodigy Learning Apprentice.- A Learning Apprentice System for VLSI Design.- Generalizing Explanations of Narratives into Schemata.- Why Are Design Derivations Hard to Replay?.- An Architecture for Experiential Learning.- Knowledge Extraction through Learning from Examples.- Learning Concepts with a Prototype-Based Model for Concept Representation.- Recent Progress on the Mathematician’s Apprentice Project.- Acquiring Domain Knowledge from Fragments of Advice.- Calm: Contestation for Argumentative Learning Machine.- Directed Experimentation for Theory Revision and Conceptual Knowledge Acquisition.- Goal-Free Learning by Analogy.- A Scientific Approach to Practical Induction.- Exploring Shifts of Representation.- Current Research on Learning in Soar.- Learning Concepts in a Complex Robot World.- Learning Evaluation Functions.- Learning from Data with Errors.- Explanation-Based Manipulator Learning.- Learning Classical Physics.- Views and Causality in Discovery: Modelling Human Induction.- Learning Control Information.- An Investigation of the Nature of Mathematical Discovery.- Learning How to Reach a Goal: A Strategy for the Multiple Classes Classification Problem.- Conceptual Clustering Of Structured Objects.- Learning in Intractable Domains.- On Compiling Explainable Models of a Design Domain.- What Can BeLearned?.- Learning Heuristic Rules from Deep Reasoning.- Learning a Domain Theory by Completing Explanations.- Learning Implementation Rules with Operating-Conditions Depending on Internal Structures in VLSI Design.- Overview of the Odysseus Learning Apprentice.- Learning from Exceptions in Databases.- Learning Apprentice Systems Research at Schlumberger.- Language Acquisition: Learning Phrases in Context.- References.Weitere, andere Bücher, die diesem Buch sehr ähnlich sein könnten:
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