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
Din seria Lecture Notes in Computer Science
- 20% Preț: 1040.03 lei
- 20% Preț: 333.46 lei
- 20% Preț: 335.08 lei
- 20% Preț: 444.17 lei
- 20% Preț: 238.01 lei
- 20% Preț: 333.46 lei
- 20% Preț: 438.69 lei
- Preț: 440.52 lei
- 20% Preț: 336.71 lei
- 20% Preț: 148.66 lei
- 20% Preț: 310.26 lei
- 20% Preț: 256.27 lei
- 20% Preț: 632.22 lei
- 17% Preț: 427.22 lei
- 20% Preț: 641.78 lei
- 20% Preț: 307.71 lei
- 20% Preț: 1053.45 lei
- 20% Preț: 579.56 lei
- Preț: 373.56 lei
- 20% Preț: 330.23 lei
- 15% Preț: 429.74 lei
- 20% Preț: 607.39 lei
- 20% Preț: 538.29 lei
- Preț: 389.48 lei
- 20% Preț: 326.98 lei
- 20% Preț: 1386.07 lei
- 20% Preț: 1003.66 lei
- 20% Preț: 567.60 lei
- 20% Preț: 575.48 lei
- 20% Preț: 571.63 lei
- 20% Preț: 747.79 lei
- 15% Preț: 568.74 lei
- 17% Preț: 360.19 lei
- 20% Preț: 504.57 lei
- 20% Preț: 172.69 lei
- 20% Preț: 369.12 lei
- 20% Preț: 346.40 lei
- 20% Preț: 574.05 lei
- Preț: 402.62 lei
- 20% Preț: 584.40 lei
- 20% Preț: 747.79 lei
- 20% Preț: 809.19 lei
- 20% Preț: 649.49 lei
- 20% Preț: 343.16 lei
- 20% Preț: 309.90 lei
- 20% Preț: 122.89 lei
Preț: 335.39 lei
Preț vechi: 419.23 lei
-20% Nou
Puncte Express: 503
Preț estimativ în valută:
64.21€ • 66.74$ • 53.23£
64.21€ • 66.74$ • 53.23£
Carte tipărită la comandă
Livrare economică 05-19 februarie 25
Preluare comenzi: 021 569.72.76
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.