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» Graph model selection using maximum likelihood
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SSD
1999
Springer
116views Database» more  SSD 1999»
13 years 12 months ago
Generalizing Graphs Using Amalgamation and Selection
This work is a contribution to the developing literature on multi-resolution data models. It considers operations for model-oriented generalization in the case where the underlying...
John G. Stell, Michael F. Worboys
ICML
2008
IEEE
14 years 8 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
ICPR
2004
IEEE
14 years 8 months ago
Robust Modelling of Local Image Structures and Its Application to Medical Imagery
A robust modelling method for detecting and measuring isotropic, linear features and bifurcations is described and applied to analysing 2d eletrophoresis and retinal images. Featu...
Li Wang, Abhir Bhalerao, Roland Wilson
TCOM
2008
108views more  TCOM 2008»
13 years 7 months ago
Performance analysis on an MAP fine timing algorithm in UWB multiband OFDM
In this paper we develop a fine synchronization algorithm for multiband OFDM transmission in the presence of frequency selective channels. This algorithm is based on maximum a post...
Christian R. Berger, Shengli Zhou, Zhi Tian, Peter...
ICNC
2005
Springer
14 years 1 months ago
Texture Segmentation Using Neural Networks and Multi-scale Wavelet Features
This paper presents a novel texture segmentation method using Bayesian estimation and neural networks. Multi-scale wavelet coefficients and the context information extracted from n...
Tae-Hyung Kim, Il Kyu Eom, Yoo Shin Kim