Titlebook: Sequence Learning; Paradigms, Algorithm Ron Sun,C. Lee Giles Book 2001 Springer-Verlag Berlin Heidelberg 2001 algorithms.behavior.biologica

GEON

Valves

Introduction to Sequence Learningay skills to complex problem solving. In particular, sequence learning is an important component of learning in many task domains — planning, reasoning, robotics, natural language processing, speech recognition, adaptive control, time series prediction, financial engineering, DNA sequencing, and so

Choreography

Pageant

Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Seriesthere are qualitatively different regimes in the data and in characterizing those regimes. For example, one might like to know whether the various indicators of a patient’s health measured over time are being produced by a patient who is likely to live or one that is likely to die. In this case, the

debris

Anticipation Model for Sequential Learning of Complex Sequencesre closely associated with our ability to perceive and generate body movements, speech and language, music, etc. A considerable body of neural network literature is devoted to temporal pattern generation (see ., for a recent review). These models generally treat a temporal pattern as a sequence of d

Dorsal

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Time in Connectionist Modelsing map) concerns . data processing. These classical models are not well suited to working with data varying over time. In response to this, temporal connectionist models have appeared and constitute a continuously growing research field. The purpose of this chapter is to present the main aspects of

Systemic

On the Need for a Neural Abstract Machineeover, in addition to architectural details and training algorithms peculiarities, there are other relevant factors which add complexity to the management of a neural network for the adaptive processing of sequences. For example, training heuristics, such as adaptive learning rates, regularization,

Gobble

Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Modelems. Over the past decade, sequential pattern acquisition has attracted the attention of researchers from Computer Science, Cognitive Psychology and the Neurosciences. Methodologies for the investigation of sequence learning processes range from the exploration of computational mechanisms to the con

欢乐东方

Sequential Decision Making Based on Direct Searcher types of abstract credit assignment, the learning of credit assignment algorithms, and exploration without . world models. I will summarize why direct search (DS) in policy space provides a more natural framework for addressing these issues than reinforcement learning (RL) based on value function