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SSC
2007
Springer
192views Cryptology» more  SSC 2007»
14 years 3 months ago
On Boolean Functions Which Are Bent and Negabent
Bent functions f : Fm 2 → F2 achieve largest distance to all linear functions. Equivalently, their spectrum with respect to the Hadamard-Walsh transform is flat (i.e. all spectr...
Matthew G. Parker, Alexander Pott
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
14 years 10 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
BMCBI
2007
95views more  BMCBI 2007»
13 years 9 months ago
Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices
Background: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-ev...
Roger A. Craig, Li Liao
ICPR
2000
IEEE
14 years 10 months ago
Image Distance Using Hidden Markov Models
We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its stati...
Daniel DeMenthon, David S. Doermann, Marc Vuilleum...
ICDM
2005
IEEE
190views Data Mining» more  ICDM 2005»
14 years 3 months ago
Adaptive Clustering: Obtaining Better Clusters Using Feedback and Past Experience
Adaptive clustering uses external feedback to improve cluster quality; past experience serves to speed up execution time. An adaptive clustering environment is proposed that uses ...
Abraham Bagherjeiran, Christoph F. Eick, Chun-Shen...