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, reasoning, robotics natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so on. This book presents coherently integrated chapters by leading authorities and assesses the state of the art in sequence learning by introducing essential models and algorithms and by examining a variety of applications. The book offers topical sections on sequence clustering and learning with Markov models, sequence prediction and recognition with neural networks, sequence discovery with symbolic methods, sequential decision making, biologically inspired sequence learning models.

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.