Evolino for recurrent support vector machines
<|reference_start|>Evolino for recurrent support vector machines: 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.<|reference_end|>
@article{schmidhuber2005evolino,
    title={Evolino for recurrent support vector machines},
    author={Juergen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino Gomez},
    journal={arXiv preprint arXiv:cs/0512062},
    year={2005},
    number={IDSIA-19-05 version 2.0},
    archivePrefix={arXiv},
    eprint={cs/0512062},
    primaryClass={cs.NE}
}