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» Support Vector Classification with Input Data Uncertainty
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96
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BMCBI
2008
171views more  BMCBI 2008»
15 years 21 days ago
A general approach to simultaneous model fitting and variable elimination in response models for biological data with many more
Background: With the advent of high throughput biotechnology data acquisition platforms such as micro arrays, SNP chips and mass spectrometers, data sets with many more variables ...
Harri T. Kiiveri
116
Voted
IJCAI
2007
15 years 2 months ago
Detection of Cognitive States from fMRI Data Using Machine Learning Techniques
Over the past decade functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful technique to locate activity of human brain while engaged in a particular task or cogni...
Vishwajeet Singh, Krishna P. Miyapuram, Raju S. Ba...
82
Voted
CORR
2010
Springer
66views Education» more  CORR 2010»
15 years 20 days ago
Efficient Dealiased Convolutions without Padding
Algorithms are developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place ...
John C. Bowman, Malcolm Roberts
156
Voted
ICASSP
2011
IEEE
14 years 4 months ago
Deep belief nets for natural language call-routing
This paper considers application of Deep Belief Nets (DBNs) to natural language call routing. DBNs have been successfully applied to a number of tasks, including image, audio and ...
Ruhi Sarikaya, Geoffrey E. Hinton, Bhuvana Ramabha...
107
Voted
NIPS
1998
15 years 1 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis