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» On learning with dissimilarity functions
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158
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ML
2008
ACM
15 years 6 months ago
A linear fit gets the correct monotonicity directions
Let f be a function on Rd that is monotonic in every variable. There are 2d possible assignments to the directions of monotonicity (two per variable). We provide sufficient condit...
Malik Magdon-Ismail, Joseph Sill
211
Voted
ML
2008
ACM
248views Machine Learning» more  ML 2008»
15 years 6 months ago
Feature selection via sensitivity analysis of SVM probabilistic outputs
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) l...
Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar ...
JCB
2007
130views more  JCB 2007»
15 years 6 months ago
Bayesian Inference of MicroRNA Targets from Sequence and Expression Data
MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although mu...
Jim C. Huang, Quaid Morris, Brendan J. Frey
STOC
2012
ACM
209views Algorithms» more  STOC 2012»
13 years 8 months ago
Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces
The Chow parameters of a Boolean function f : {−1, 1}n → {−1, 1} are its n + 1 degree-0 and degree-1 Fourier coefficients. It has been known since 1961 [Cho61, Tan61] that ...
Anindya De, Ilias Diakonikolas, Vitaly Feldman, Ro...
BSN
2006
IEEE
131views Sensor Networks» more  BSN 2006»
16 years 6 days ago
Elaborating Sensor Data using Temporal and Spatial Commonsense Reasoning
Ubiquitous computing has established a vision of computation where computers are so deeply integrated into our lives that they become both invisible and everywhere. In order to ha...
Bo Morgan, Push Singh