Machine learning : ECML-98 : 10th European Conference on Machine Learning Chemnitz, Germany, April 21 23, 1998 : proceedings
This book constitutes the refereed proceedings of the 10th European Conference on Machine Learning, ECML-98, held in Chemnitz, Germany, in April 1998. The book presents 21 revised full papers and 25 short papers reporting on work in progress together with two invited contributions; the papers were s...
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| Tác giả của công ty: | |
| Tác giả khác: | |
| Định dạng: | Livre numérique |
| Ngôn ngữ: | Anglais |
| Được phát hành: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Loạt: | Lecture notes in computer science. Lecture notes in artificial intelligence
1398 |
| Những chủ đề: | |
| Truy cập trực tuyến: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Chú thích: |
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-98, Texte imprimé, 9783540644170 • Machine Learning: ECML-98, Texte imprimé, 9783662202531 |
Mục lục:
- Learning in agent-oriented worlds
- Naive (Bayes) at forty: The independence assumption in information retrieval
- Learning verbal transitivity using loglinear models
- Part-of-speech tagging using decision trees
- Inference of finite automata: Reducing the search space with an ordering of pairs of states
- Automatic acquisition of lexical knowledge from sparse and noisy data
- A normalization method for contextual data: Experience from a large-scale application
- Learning to classify x-ray images using relational learning
- ILP experiments in detecting traffic problems
- Simulating children learning and explaining elementary heat transfer phenomena: A multistrategy system at work
- Bayes optimal instance-based learning
- Bayesian and information-theoretic priors for Bayesian network parameters
- Feature subset selection in text-learning
- A monotonic measure for optimal feature selection
- Inducing models of human control skills
- God doesn't always shave with Occam's razor Learningwhen and how to prune
- Error estimators for pruning regression trees
- Pruning decision trees with misclassification costs
- Text categorization with Support Vector Machines: Learning with many relevant features
- A short note about the application of polynomial kernels with fractional degree in Support Vector Learning
- Classification learning using all rules
- Improved pairwise coupling classification with correcting classifiers
- Experiments on solving multiclass learning problems by n 2-classifier
- Combining classifiers by constructive induction
- Boosting trees for cost-sensitive classifications
- Naive bayesian classifier committees
- Batch classifications with discrete finite mixtures
- Induction of recursive program schemes
- Predicate invention and learning from positive examples only
- An inductive logic programming framework to learn a concept from ambiguous examples
- First-order learning for Web mining
- Explanation-based generalization in game playing: Quantitative results
- Scope classification: An instance-based learning algorithm with a rule-based characterisation
- Error-correcting output codes for local learners
- Recursive lazy learning for modeling and control
- Using lattice-based framework as a tool for feature extraction
- Determining property relevance in concept formation by computing correlation between properties
- A buffering strategy to avoid ordering effects in clustering
- Coevolutionary, distributed search for inducing concept descriptions
- Continuous mimetic evolution
- A host-parasite genetic algorithm for asymmetric tasks
- Speeding up Q(?)-learning
- Q-learning and redundancy reduction in classifier systems with internal state
- Composing functions to speed up reinforcement learning in a changing world
- Theoretical results on reinforcement learning with temporally abstract options
- A general convergence method for Reinforcement Learning in the continuous case
- Interpretable neural networks with BP-SOM
- Convergence rate ofminimization learning for neural networks.