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» A fast algorithm for computing large Fibonacci numbers
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KDD
2002
ACM
182views Data Mining» more  KDD 2002»
16 years 4 months ago
ANF: a fast and scalable tool for data mining in massive graphs
Graphs are an increasingly important data source, with such important graphs as the Internet and the Web. Other familiar graphs include CAD circuits, phone records, gene sequences...
Christopher R. Palmer, Phillip B. Gibbons, Christo...
ECCV
2006
Springer
16 years 5 months ago
Integrating Surface Normal Vectors Using Fast Marching Method
i Integration of surface normal vectors is a vital component in many shape reconstruction algorithms that require integrating surface normals to produce their final outputs, the de...
Jeffrey Ho, Jongwoo Lim, Ming-Hsuan Yang, David J....
ICML
2009
IEEE
16 years 4 months ago
Prototype vector machine for large scale semi-supervised learning
Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised...
Kai Zhang, James T. Kwok, Bahram Parvin
ICCAD
2007
IEEE
137views Hardware» more  ICCAD 2007»
16 years 27 days ago
Analysis of large clock meshes via harmonic-weighted model order reduction and port sliding
— Clock meshes posses inherent low clock skews and excellent immunity to PVT variations, and have increasingly found their way to high-performance IC designs. However, analysis o...
Xiaoji Ye, Peng Li, Min Zhao, Rajendran Panda, Jia...
SIGECOM
2006
ACM
143views ECommerce» more  SIGECOM 2006»
15 years 10 months ago
Braess's paradox in large random graphs
Braess’s Paradox is the counterintuitive but well-known fact that removing edges from a network with “selfish routing” can decrease the latency incurred by traffic in an eq...
Gregory Valiant, Tim Roughgarden