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Machine Learning: ECML-94: European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings de Francesco Bergadano

Machine Learning: ECML-94: European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings: Lecture Notes in Computer Science, cartea 784

Editat de Francesco Bergadano, Luc de Raedt
en Limba Engleză Paperback – 22 mar 1994
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 of the latest and most significant results in machine learning.
Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes.
This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions.
The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.
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Specificații

ISBN-13: 9783540578680
ISBN-10: 3540578684
Pagini: 460
Ilustrații: XIII, 447 p.
Dimensiuni: 155 x 233 x 24 mm
Greutate: 0.64 kg
Ediția:1994
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.