Machine Learning: ECML-93: European Conference on Machine Learning, Vienna, Austria, April 5-7, 1993. Proceedings: Lecture Notes in Computer Science, cartea 667
Editat de Pavel B. Brazdilen Limba Engleză Paperback – 23 mar 1993
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Specificații
ISBN-13: 9783540566021
ISBN-10: 3540566023
Pagini: 492
Ilustrații: XII, 480 p.
Dimensiuni: 156 x 234 x 26 mm
Greutate: 0.69 kg
Ediția:1993
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3540566023
Pagini: 492
Ilustrații: XII, 480 p.
Dimensiuni: 156 x 234 x 26 mm
Greutate: 0.69 kg
Ediția:1993
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
FOIL: A midterm report.- Inductive logic programming: Derivations, successes and shortcomings.- Two methods for improving inductive logic programming systems.- Generalization under implication by using or-introduction.- On the proper definition of minimality in specialization and theory revision.- Predicate invention in inductive data engineering.- Subsumption and refinement in model inference.- Some lower bounds for the computational complexity of inductive logic programming.- Improving example-guided unfolding.- Bayes and pseudo-Bayes estimates of conditional probabilities and their reliability.- Induction of recursive Bayesian classifiers.- Decision tree pruning as a search in the state space.- Controlled redundancy in incremental rule learning.- Getting order independence in incremental learning.- Feature selection using rough sets theory.- Effective learning in dynamic environments by explicit context tracking.- COBBIT—A control procedure for COBWEB in the presence of concept drift.- Genetic algorithms for protein tertiary structure prediction.- SIA: A supervised inductive algorithm with genetic search for learning attributes based concepts.- SAMIA: A bottom-up learning method using a simulated annealing algorithm.- Predicate invention in ILP — an overview.- Functional inductive logic programming with queries to the user.- A note on refinement operators.- An iterative and bottom-up procedure for proving-by-example.- Learnability of constrained logic programs.- Complexity dimensions and learnability.- Can complexity theory benefit from Learning Theory?.- Learning domain theories using abstract background knowledge.- Discovering patterns in EEG-signals: Comparative study of a few methods.- Learning to control dynamic systems with automatic quantization.- Refinement of rule sets with JoJo.- Rule combination in inductive learning.- Using heuristics to speed up induction on continuous-valued attributes.- Integrating models of knowledge and Machine Learning.- Exploiting context when learning to classify.- IDDD: An inductive, domain dependent decision algorithm.- An application of machine learning in the domain of loan analysis.- Extraction of knowledge from data using constrained neural networks.- Integrated learning architectures.- An overview of evolutionary computation.- ML techniques and text analysis.