Computationally Efficient Algorithms for Sparse, Dynamic Solutions to the EEG Source Localization Problem

2018 ◽  
Vol 65 (6) ◽  
pp. 1359-1372 ◽  
Author(s):  
Elvira Pirondini ◽  
Behtash Babadi ◽  
Gabriel Obregon-Henao ◽  
Camilo Lamus ◽  
Wasim Q. Malik ◽  
...  
Keyword(s):  
Source Localization ◽  
Efficient Algorithms ◽  
Localization Problem ◽  
Computationally Efficient ◽  
Eeg Source Localization ◽  
Computationally Efficient Algorithms

Computationally efficient algorithms for location area planning in future cellular systems

2000 ◽  
Vol 23 (13) ◽  
pp. 1263-1280 ◽  
Author(s):  
P. Demestichas ◽  
N. Georgantas ◽  
E. Tzifa ◽  
V. Demesticha ◽  
M. Striki ◽  
...  
Keyword(s):  
Efficient Algorithms ◽  
Cellular Systems ◽  
Computationally Efficient ◽  
Location Area ◽  
Computationally Efficient Algorithms ◽  
Area Planning

scholarly journals Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem

NeuroImage ◽  
2002 ◽  
Vol 17 (1) ◽  
pp. 287-301 ◽  
Author(s):  
Christophe Phillips ◽  
Michael D. Rugg ◽  
Karl J. Friston
Keyword(s):  
Source Localization ◽  
Localization Problem ◽  
Inverse Solutions ◽  
Eeg Source Localization

UNIVERSAL ALGORITHMS FOR PROBABILITY FORECASTING

2012 ◽  
Vol 21 (04) ◽  
pp. 1240015
Author(s):  
FEDOR ZHDANOV ◽  
YURI KALNISHKAN
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Linear Model ◽  
Loss Function ◽  
Efficient Algorithms ◽  
Classification Problems ◽  
Computationally Efficient ◽  
Multi Class Classification ◽  
Computationally Efficient Algorithms

Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a linear model and by a kernelized model. The quality of predictions is measured by the Brier loss function. We obtain two computationally efficient algorithms for these problems by applying the Aggregating Algorithms to certain pools of experts and prove theoretical guarantees on the losses of these algorithms. We kernelize one of the algorithms and prove theoretical guarantees on its loss. We perform experiments and compare our algorithms with logistic regression.

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