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» Approximating the Number of Network Motifs
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GECCO
2004
Springer
116views Optimization» more  GECCO 2004»
15 years 11 months ago
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
Abstract. In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models c...
Yaochu Jin, Bernhard Sendhoff
GECCO
2007
Springer
558views Optimization» more  GECCO 2007»
16 years 8 days ago
A chain-model genetic algorithm for Bayesian network structure learning
Bayesian Networks are today used in various fields and domains due to their inherent ability to deal with uncertainty. Learning Bayesian Networks, however is an NP-Hard task [7]....
Ratiba Kabli, Frank Herrmann, John McCall
GLOBECOM
2009
IEEE
16 years 26 days ago
Data Acquisition through Joint Compressive Sensing and Principal Component Analysis
—In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniqu...
Riccardo Masiero, Giorgio Quer, Daniele Munaretto,...
176
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ESANN
2007
15 years 7 months ago
Adaptive Global Metamodeling with Neural Networks
Due to the scale and computational complexity of current simulation codes, metamodels (or surrogate models) have become indispensable tools for exploring and understanding the desi...
Dirk Gorissen, Wouter Hendrickx, Tom Dhaene
DMSN
2004
ACM
15 years 11 months ago
Predictive filtering: a learning-based approach to data stream filtering
Recent years have witnessed an increasing interest in filtering of distributed data streams, such as those produced by networked sensors. The focus is to conserve bandwidth and se...
Vibhore Kumar, Brian F. Cooper, Shamkant B. Navath...