Sequence Learning: Paradigms, Algorithms, and Applications (Paperback, 2001)

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.