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

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0302-9743 ns 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.978-3-540-41597-8978-3-540-44565-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Anticipation Model for Sequential Learning of Complex Sequencesnts, part of the input layer is used to keep a trace of history, behaving as short-term memory (STM). Similarly, for temporal recall based on the Hopfield associative memory, a temporal sequence is viewed as associations between consecutive components. These associations are stored in extended versi

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Bidirectional Dynamics for Protein Secondary Structure Prediction . cannot depend on stimulae that the system has not yet received as input. As it turns out, non-causal dynamics over infinite time horizons cannot be realized by any physical or computational device. For certain categories of . sequences, however, information from both the past and the future can b

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