Neural Comput - Regularized variational Bayesian learning of echo state networks with delay&sum readout.

Tópicos

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Resumo

In this work, a variational Bayesian framework for efficient training of echo state networks (ESNs) with automatic regularization and delay&sum (D&S) readout adaptation is proposed. The algorithm uses a classical batch learning of ESNs. By treating the network echo states as fixed basis functions parameterized with delay parameters, we propose a variational Bayesian ESN training scheme. The variational approach allows for a seamless combination of sparse Bayesian learning ideas and a variational Bayesian space-alternating generalized expectation-maximization (VB-SAGE) algorithm for estimating parameters of superimposed signals. While the former method realizes automatic regularization of ESNs, which also determines which echo states and input signals are relevant for "explaining" the desired signal, the latter method provides a basis for joint estimation of D&S readout parameters. The proposed training algorithm can naturally be extended to ESNs with fixed filter neurons. It also generalizes the recently proposed expectation-maximization-based D&S readout adaptation method. The proposed algorithm was tested on synthetic data prediction tasks as well as on dynamic handwritten character recognition.

Resumo Limpo

work variat bayesian framework effici train echo state network esn automat regular delaysum ds readout adapt propos algorithm use classic batch learn esn treat network echo state fix basi function parameter delay paramet propos variat bayesian esn train scheme variat approach allow seamless combin spars bayesian learn idea variat bayesian spacealtern general expectationmaxim vbsage algorithm estim paramet superimpos signal former method realiz automat regular esn also determin echo state input signal relev explain desir signal latter method provid basi joint estim ds readout paramet propos train algorithm can natur extend esn fix filter neuron also general recent propos expectationmaximizationbas ds readout adapt method propos algorithm test synthet data predict task well dynam handwritten charact recognit

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