Sequence learning : paradigms, algorithms, and applications
Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many tasks and application fields: planning, rea...
Salvato in:
| Autore principale: | |
|---|---|
| Altri autori: | |
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
1828 |
| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Sequence learning, paradigms, algorithms, and applications, Ron Sun, C. Lee Giles (eds.), New York, Springer, 2001, 1 vol. (XII-387 p.), Lecture notes in computer science, 3-540-41597-1 • Sequence Learning, Texte imprimé, 9783662186145 |
Sommario:
- to Sequence Learning
- to Sequence Learning
- Sequence Clustering and Learning with Markov Models
- Sequence Learning via Bayesian Clustering by Dynamics
- Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
- Sequence Prediction and Recognition with Neural Networks
- Anticipation Model for Sequential Learning of Complex Sequences
- Bidirectional Dynamics for Protein Secondary Structure Prediction
- Time in Connectionist Models
- On the Need for a Neural Abstract Machine
- Sequence Discovery with Symbolic Methods
- Sequence Mining in Categorical Domains: Algorithms and Applications
- Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model
- Sequential Decision Making
- Sequential Decision Making Based on Direct Search
- Automatic Segmentation of Sequences through Hierarchical Reinforcement Learning
- Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making
- Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning
- Biologically Inspired Sequence Learning Models
- Multiple Forward Model Architecture for Sequence Processing
- Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing
- Attentive Learning of Sequential Handwriting Movements: A Neural Network Model.