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IJON
2007
184views more  IJON 2007»
15 years 7 months ago
Convex incremental extreme learning machine
Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden n...
Guang-Bin Huang, Lei Chen
210
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IOR
2007
106views more  IOR 2007»
15 years 7 months ago
Planning and Scheduling by Logic-Based Benders Decomposition
We combine mixed integer linear programming (MILP) and constraint programming (CP) to solve an important class of planning and scheduling problems. Tasks are allocated to faciliti...
John N. Hooker
TIFS
2008
102views more  TIFS 2008»
15 years 7 months ago
Insertion, Deletion Codes With Feature-Based Embedding: A New Paradigm for Watermark Synchronization With Applications to Speech
A framework is proposed for synchronization in feature-based data embedding systems that is tolerant of errors in estimated features. The method combines feature-based embedding wi...
David J. Coumou, Gaurav Sharma
211
Voted
TIP
2008
169views more  TIP 2008»
15 years 7 months ago
Maximum Likelihood Wavelet Density Estimation With Applications to Image and Shape Matching
Density estimation for observational data plays an integral role in a broad spectrum of applications, e.g. statistical data analysis and information-theoretic image registration. ...
Adrian M. Peter, Anand Rangarajan
209
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TIP
2008
133views more  TIP 2008»
15 years 7 months ago
A Recursive Model-Reduction Method for Approximate Inference in Gaussian Markov Random Fields
This paper presents recursive cavity modeling--a principled, tractable approach to approximate, near-optimal inference for large Gauss-Markov random fields. The main idea is to su...
Jason K. Johnson, Alan S. Willsky
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