Evolino for recurrent support vector machines

Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino Gomez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


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 networks to detect and represent temporal dependencies while using SVM 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.
Original languageEnglish (US)
Title of host publicationESANN 2006 Proceedings - European Symposium on Artificial Neural Networks
Publisherd-side publication
Number of pages6
ISBN (Print)2930307064
StatePublished - Jan 1 2006
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14


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