Algorithmic learning theory : 4th international workshop, ALT '93 Tokyo, Japan, November 8-10, 1993 : proceedings
This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In addition to 3 invited papers, 29 papers were selected from 47 submitted extended abstracts. The workshop was the fourth in a series of ALT workshops, whos...
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| Outros autores: | , , |
| Formato: | Livre numérique |
| Idioma: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Series: | Lecture notes in computer science. Lecture notes in artificial intelligence
744 |
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| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Autre directeur de publication : T. Yokomori Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Algorithmic learning theory, 4th international workshop, ALT '93, Tokyo, Japan, November 8-10, 1993, proceedings, editors Klaus P. Jantke, Shigenobu Kobayashi, Etsuji Tomita... [et al.], 1993, Berlin, Springer, 1 vol. (XI-423 p.), Lecture notes in computer science, 3-540-57370-4 • Algorithmic Learning Theory, Texte imprimé, 9783662206652 |
Table des matières:
- Identifying and using patterns in sequential data
- Learning theory toward Genome Informatics
- Optimal layered learning: A PAC approach to incremental sampling
- Reformulation of explanation by linear logic toward logic for explanation
- Towards efficient inductive synthesis of expressions from input/output examples
- A typed ?-calculus for proving-by-example and bottom-up generalization procedure
- Case-based representation and learning of pattern languages
- Inductive resolution
- Generalized unification as background knowledge in learning logic programs
- Inductive inference machines that can refute hypothesis spaces
- On the duality between mechanistic learners and what it is they learn
- On aggregating teams of learning machines
- Learning with growing quality
- Use of reduction arguments in determining Popperian FIN-type learning capabilities
- Properties of language classes with finite elasticity
- Uniform characterizations of various kinds of language learning
- How to invent characterizable inference methods for regular languages
- Neural Discriminant Analysis
- A new algorithm for automatic configuration of Hidden Markov Models
- On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions
- On the sample complexity of consistent learning with one-sided error
- Complexity of computing Vapnik-Chervonenkis dimension
- ?-approximations of k-label spaces
- Exact learning of linear combinations of monotone terms from function value queries
- Thue systems and DNA A learning algorithm for a subclass
- The VC-dimensions of finite automata with n states
- Unifying learning methods by colored digraphs
- A perceptual criterion for visually controlling learning
- Learning strategies using decision lists
- A decomposition basedinduction model for discovering concept clusters from databases
- Algebraic structure of some learning systems
- Induction of probabilistic rules based on rough set theory.