2013 TreeEchoStateNetworks

• (Gallicchio & Micheli, 2013) ⇒ Claudio Gallicchio, and Alessio Micheli. (2013). “Tree Echo State Networks.” In: Neurocomputing Journal, 101. doi:10.1016/j.neucom.2012.08.017

Subject Headings: Tree Echo State Network, Echo State Network, Reservoir Computing.

Cited By

• Google Scholar: ~ 43 Citations (Retrieved:2019-10-27).
• ACM DL: 4 Citations (Retrieved:2019-10-27).
• Semantic Scholar: ~ 18 Citations (Retrieved:2019-10-27).
• MS Academic: ~ 32 Citations (Retrieved:2019-10-27).

Quotes

Abstract

In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach: First, we discuss the Markovian characterization of reservoir dynamics, extended to the case of tree domains, that is implied by the contractive setting of the TreeESN state transition function. Second, we study two types of state mapping functions to map the tree structured state of TreeESN into a fixed-size feature representation for classification or regression tasks. The critical role of the relation between the choice of the state mapping function and the Markovian characterization of the task is analyzed and experimentally investigated on both artificial and real-world tasks. Finally, experimental results on benchmark and real-world tasks show that the TreeESN approach, in spite of its efficiency, can achieve comparable results with state-of-the-art, although more complex, neural and kernel based models for tree structured data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 TreeEchoStateNetworks	Claudio Gallicchio Alessio Micheli			Tree Echo State Networks				10.1016/j.neucom.2012.08.017		2013