LAP Lambert Academic Publishing
LSTM Recurrent Neural Networks for Signature Verification
Product Code:
9783846589946
ISBN13:
9783846589946
Condition:
New
$53.25
LSTM Recurrent Neural Networks for Signature Verification
$53.25
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.
| Author: Conrad Tiflin |
| Publisher: LAP Lambert Academic Publishing |
| Publication Date: Feb 06, 2012 |
| Number of Pages: 104 pages |
| Binding: Paperback or Softback |
| ISBN-10: 3846589942 |
| ISBN-13: 9783846589946 |
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