Isbn: 9783846589946 - lstm recurrent neural networks for signature verification: a novel approach (11 resultados)

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Taschenbuch. Condición: Neu. LSTM Recurrent Neural Networks for Signature Verification | A Novel Approach | Conrad Tiflin | Taschenbuch | 104 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783846589946 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. …

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tiflin ConradConrad completed his Masters Degree with Cum Laude with the Intelligent Systems Group of the Department of Computer Science at the University of the Western Cape. His research interests span Recurrent Neural Networks (RN.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…