LSTM Recurrent Neural Networks for Signature Verification : A Novel Approach

Language: English

Published by LAP LAMBERT Academic Publishing, 2012

3846589942 / 9783846589946

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nach der Bestellung gedruckt Neuware - Printed after ordering - The author investigated the application of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) to the task of signature verification. Traditional RNNs are capable of modeling dynamical systems with hidden states; they have been successfully applied to domains ranging from financial forecasting to control and speech recognition. This manuscript is the result of successfully applying on-line signature time series data to traditional LSTM, LSTM with forget gates and LSTM with peephole connections algorithms originally developed by S. Hochreiter and J. Schmidhuber. It can be clearly seen in this pattern classification problem that traditional LSTM RNNs outperform LSTMs with forget gates and peephole connections. The latter also outperform traditional RNNs which cannot seem to even learn this task due to the long-term dependency problem.

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Title
LSTM Recurrent Neural Networks for Signature Verification : A Novel Approach
Author
Conrad Tiflin
Publisher
LAP LAMBERT Academic Publishing
Publication year
2012
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3846589942
ISBN 13
9783846589946
Item weight
173 grams
Dimensions
220x150x7 mm

AHA-BUCH GmbH

Einbeck, Germany

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