Machine learning : ECML-97 : 9th European Conference on Machine Learning Prague, Czech Republic, April 23 25, 1997 : proceedings
This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding...
में बचाया:
| मुख्य लेखक: | |
|---|---|
| निगमित लेखक: | |
| अन्य लेखक: | |
| स्वरूप: | Livre numérique |
| भाषा: | Anglais |
| प्रकाशित: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| श्रृंखला: | Lecture notes in computer science. Lecture notes in artificial intelligence
1224 |
| विषय: | |
| ऑनलाइन पहुंच: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| टिप्पणी: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Machine learning: ECML-97, 9th European Conference on Machine Learning, Prague, Czech Republic, April 1997, proceedings, Maarten van Someren, Gerhard Widmer (eds.), 1997, Berlin, Springer, 1 vol. (XI-360 p.), Lecture notes in computer science, 3-540-62858-4 • Machine Learning: ECML'97, Texte imprimé, 9783662203620 |
विषय - सूची:
- Uncertain learning agents
- Constructing and sharing perceptual distinctions
- On prediction by data compression
- Induction of feature terms with INDIE
- Exploiting qualitative knowledge to enhance skill acquisition
- Integrated learning and planning based on truncating temporal differences
- ?-subsumption for structural matching
- Classification by Voting Feature Intervals
- Constructing intermediate concepts by decomposition of real functions
- Conditions for Occam's razor applicability and noise elimination
- Learning different types of new attributes by combining the neural network and iterative attribute construction
- Metrics on terms and clauses
- Learning when negative examples abound
- A model for generalization based on confirmatory induction
- Learning Linear Constraints in Inductive Logic Programming
- Finite-Element methods with local triangulation refinement for continuous reinforcement learning problems
- Inductive Genetic Programming with Decision Trees
- Parallel anddistributed search for structure in multivariate time series
- Compression-based pruning of decision lists
- Probabilistic Incremental Program Evolution: Stochastic search through program space
- NeuroLinear: A system for extracting oblique decision rules from neural networks
- Inducing and using decision rules in the GRG knowledge discovery system
- Learning and exploitation do not conflict under minimax optimality
- Model combination in the multiple-data-batches scenario
- Search-based class discretization
- Natural ideal operators in Inductive Logic Programming
- A case study in loyalty and satisfaction research
- Ibots learn genuine team solutions
- Global data analysis and the fragmentation problem in decision tree induction
- Case-based learning: Beyond classification of feature vectors
- Empirical learning of Natural Language Processing tasks
- Human-Agent Interaction and Machine Learning
- Learning in dynamically changing domains: Theory revision and context dependence issues.