Application of Information Theory and Learning to Network and Biological Tomography

Application of Information Theory and Learning to Network and Biological Tomography

Studying the internal characteristics of a network using measurements obtained from endhosts is known as network tomography. The foremost challenge in measurement-based approaches is the large size of a network, where only a subset of measurements can be obtained because of the inaccessibility of th...

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Bibliographic Details
Main Author: Narasimha, Rajesh
Language:en_US
Published: Georgia Institute of Technology 2008
Subjects:
Bounds
Inference
Network tomography
Bayesian belief networks
LDPC codes
Scalability
Active probing
Electron tomography
End-to end measurements
Classification
Melanoma
Texture features
Segmentation
HIV-1
Denoising
Machine learning
Information theory
Cancer detection
Network performance (Telecommunication)
Computer simulation
Computer networks Scalability
Tomography
Transmission electron microscopy
Online Access:http://hdl.handle.net/1853/19889

Internet

http://hdl.handle.net/1853/19889

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