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ISBN: 9783540483656

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation… Mehr…

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Machine Learning: ECML-94 - neues Buch

ISBN: 9783540483656

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation… Mehr…

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Machine Learning: ECML-94 - neues Buch

ISBN: 9783540483656

Computer Science; Artificial Intelligence (incl. Robotics) Algorithmic Learning, Algorithmisches Lernen, Inductive Learning, Inductive Logic Programming, Induktives Lernen, Induktives log… Mehr…

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Machine Learning: ECML-94 - neues Buch

ISBN: 9783540483656

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Machine Learning: ECML-94
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9783540483656

Detailangaben zum Buch - Machine Learning: ECML-94


EAN (ISBN-13): 9783540483656
Herausgeber: Springer Science+Business Media

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ISBN/EAN: 9783540483656

ISBN - alternative Schreibweisen:
978-3-540-48365-6
Alternative Schreibweisen und verwandte Suchbegriffe:
Titel des Buches: machine learning

Daten vom Verlag:

Autor/in: Francesco Bergadano; Luc de Raedt
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning: ECML-94 - European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings
Verlag: Springer; Springer Berlin
447 Seiten
Erscheinungsjahr: 2005-07-07
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
55,00 € (AT)
59,00 CHF (CH)
Available
XIII, 447 p.

EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Algorithmic Learning; Algorithmisches Lernen; Inductive Learning; Inductive Logic Programming; Induktives Lernen; Induktives logisches Programmieren; Multi-strategy Learning; artificial intelligence; automation; classification; complexity; genetic programming; knowledge representation; learning; machine learning; C; Artificial Intelligence; Computer Science; BC

Industrial applications of ML: Illustrations for the KAML dilemma and the CBR dream.- Knowledge representation in machine learning.- Inverting implication with small training sets.- A context similarity measure.- Incremental learning of control knowledge for nonlinear problem solving.- Characterizing the applicability of classification algorithms using meta-level learning.- Inductive learning of characteristic concept descriptions from small sets of classified examples.- FOSSIL: A robust relational learner.- A multistrategy learning system and its integration into an interactive floorplanning tool.- Bottom-up induction of oblivious read-once decision graphs.- Estimating attributes: Analysis and extensions of RELIEF.- BMWk revisited generalization and formalization of an algorithm for detecting recursive relations in term sequences.- An analytic and empirical comparison of two methods for discovering probabilistic causal relationships.- Sample PAC-learnability in model inference.- Averaging over decision stumps.- Controlling constructive induction in CIPF: An MDL approach.- Using constraints to building version spaces.- On the utility of predicate invention in inductive logic programming.- Learning problem-solving concepts by reflecting on problem solving.- Existence and nonexistence of complete refinement operators.- A hybrid nearest-neighbor and nearest-hyperrectangle algorithm.- Automated knowledge acquisition for Prospector-like expert systems.- On the role of machine learning in knowledge-based control.- Discovering dynamics with genetic programming.- A geometric approach to feature selection.- Identifying unrecognizable regular languages by queries.- Intensional learning of logic programs.- Partially isomorphic generalization and analogical reasoning.- Learning from recursive, tree structured examples.- Concept formation in complex domains.- An algorithm for learning hierarchical classifiers.- Learning belief network structure from data under causal insufficiency.- Cost-sensitive pruning of decision trees.- An instance-based learning method for databases: An information theoretic approach.- Early screening for gastric cancer using machine learning techniques.- DP1: Supervised and unsupervised clustering.- Using machine learning techniques to interpret results from discrete event simulation.- Flexible integration of multiple learning methods into a problem solving architecture.- Concept sublattices.- The piecewise linear classifier DIPOL92.- Complexity of computing generalized VC-dimensions.- Learning relations without closing the world.- Properties of Inductive Logic Programming in function-free Horn logic.- Representing biases for Inductive Logic Programming.- Biases and their effects in Inductive Logic Programming.- Inductive learning of normal clauses.
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