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» Mixed Models for the Analysis of Local Search Components
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INFOCOM
2002
IEEE
14 years 10 days ago
Increasing robustness of fault localization through analysis of lost, spurious, and positive symptoms
—This paper utilizes belief networks to implement fault localization in communication systems taking into account comprehensive information about the system behavior. Most previo...
Malgorzata Steinder, Adarshpal S. Sethi
PPSN
2010
Springer
13 years 5 months ago
First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes
Abstract. This paper extends a recently proposed model for combinatorial landscapes: Local Optima Networks (LON), to incorporate a first-improvement (greedyascent) hill-climbing a...
Gabriela Ochoa, Sébastien Vérel, Mar...
ICML
2004
IEEE
14 years 8 months ago
Automated hierarchical mixtures of probabilistic principal component analyzers
Many clustering algorithms fail when dealing with high dimensional data. Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it assumes a ...
Ting Su, Jennifer G. Dy
ISBI
2007
IEEE
14 years 1 months ago
An Effective and Efficient Technique for Searching for Similar Brain Activation Patterns
In this paper, we introduce a new approach for content-based similarity search for brain images. Based on the keyblock representation, our framework employs the Principal Componen...
Jingjing Zhang, Vasileios Megalooikonomou
CIKM
2010
Springer
13 years 6 months ago
Decomposing background topics from keywords by principal component pursuit
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools s...
Kerui Min, Zhengdong Zhang, John Wright, Yi Ma