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    block this user Matteo Gagliolo

    Post Doctorate

    CoMo, VUB, Brussels

    Evolino for recurrent support vector machines

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    Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.

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    Description

    Title : Evolino for recurrent support vector machines
    Author(s) : Juergen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino Gomez
    Abstract : Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.
    Keywords : neural evolutionary computing

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2005

    Affiliations CoMo, VUB, Brussels
    Editors : Michel Verleysen
    Journal : Evolution
    Volume : 05
    Issue : December
    Publisher : d-side
    Pages : 10
    Url : http://arxiv.org/abs/cs/0512062

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