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IJCNN
2000
IEEE
15 years 4 months ago
ICA for Noisy Neurobiological Data
ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (...
Shiro Ikeda, Keisuke Toyama
NN
2000
Springer
159views Neural Networks» more  NN 2000»
14 years 11 months ago
Independent component analysis for noisy data -- MEG data analysis
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such ...
Shiro Ikeda, Keisuke Toyama
JSC
2006
102views more  JSC 2006»
14 years 11 months ago
Counting and locating the solutions of polynomial systems of maximum likelihood equations, I
In statistics, mixture models consisting of several component subpopulations are used widely to model data drawn from heterogeneous sources. In this paper, we consider maximum lik...
Max-Louis G. Buot, Donald St. P. Richards
ISBI
2006
IEEE
16 years 14 days ago
Sample dependence correction for order selection in fMRI analysis
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study the brai...
Tülay Adali, Vince D. Calhoun, Yi-Ou Li
FOCS
2008
IEEE
15 years 6 months ago
Computing the Tutte Polynomial in Vertex-Exponential Time
The deletion–contraction algorithm is perhaps the most popular method for computing a host of fundamental graph invariants such as the chromatic, flow, and reliability polynomi...
Andreas Björklund, Thore Husfeldt, Petteri Ka...