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AUTOENCODER NEURAL NETWORKS
A Performance Study Based on Image Reconstruction, Recognition and Compression
Taschenbuch von Chun Chet Tan (u. a.)
Sprache: Englisch

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Beschreibung
Autoencoders are feedforward neural networks which can have more than one hidden layer. These networks attempt to reconstruct the input data at the output layer. Since the size of the hidden layer in the autoencoders is smaller than the size of the input data, the dimensionality of input data is reduced to a smaller-dimensional code space at the hidden layer. However, training a multilayer autoencoder is tedious. This is due to the fact that the weights at deep hidden layers are hardly optimized. The research work has focused on the characteristics, training and performance evaluation of autoencoders. The concepts of stacking and Restricted Boltzmann Machine have also been discussed in detail. Two datasets, namely ORL face dataset and MNIST handwritten digit dataset have been employed in these experiments. The performances of the autoencoders have also been compared with that of PCA. It has been shown that the autoencoders can also be used for image compression. The compression efficiency has been studied using DDSM dataset (mammogram dataset). Since image patches were used for training, it was possible to compress and decompress mammograms of different sizes.
Autoencoders are feedforward neural networks which can have more than one hidden layer. These networks attempt to reconstruct the input data at the output layer. Since the size of the hidden layer in the autoencoders is smaller than the size of the input data, the dimensionality of input data is reduced to a smaller-dimensional code space at the hidden layer. However, training a multilayer autoencoder is tedious. This is due to the fact that the weights at deep hidden layers are hardly optimized. The research work has focused on the characteristics, training and performance evaluation of autoencoders. The concepts of stacking and Restricted Boltzmann Machine have also been discussed in detail. Two datasets, namely ORL face dataset and MNIST handwritten digit dataset have been employed in these experiments. The performances of the autoencoders have also been compared with that of PCA. It has been shown that the autoencoders can also be used for image compression. The compression efficiency has been studied using DDSM dataset (mammogram dataset). Since image patches were used for training, it was possible to compress and decompress mammograms of different sizes.
Über den Autor
C. C. Tan: received B.Sc. degree from University of Malaya, and M.IT degree from Multimedia University, Malaysia. He is a lecturer in Faculty of I.T., Multimedia University. Dr. C. Eswaran: received B.Tech, M.Tech and Ph.D degrees from the Indian Institute of Technolgy Madras, India. He is a Professor in Faculty of I.T., Multimedia University.
Details
Erscheinungsjahr: 2010
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: 96 S.
ISBN-13: 9783838309460
ISBN-10: 3838309464
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Tan, Chun Chet
Eswaran, Chikkannan
Hersteller: LAP LAMBERT Academic Publishing
Maße: 220 x 150 x 6 mm
Von/Mit: Chun Chet Tan (u. a.)
Erscheinungsdatum: 21.05.2010
Gewicht: 0,161 kg
Artikel-ID: 101501840
Über den Autor
C. C. Tan: received B.Sc. degree from University of Malaya, and M.IT degree from Multimedia University, Malaysia. He is a lecturer in Faculty of I.T., Multimedia University. Dr. C. Eswaran: received B.Tech, M.Tech and Ph.D degrees from the Indian Institute of Technolgy Madras, India. He is a Professor in Faculty of I.T., Multimedia University.
Details
Erscheinungsjahr: 2010
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: 96 S.
ISBN-13: 9783838309460
ISBN-10: 3838309464
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Tan, Chun Chet
Eswaran, Chikkannan
Hersteller: LAP LAMBERT Academic Publishing
Maße: 220 x 150 x 6 mm
Von/Mit: Chun Chet Tan (u. a.)
Erscheinungsdatum: 21.05.2010
Gewicht: 0,161 kg
Artikel-ID: 101501840
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