Computationally efficient algorithms to enhance multiple configuration systems

2021 ◽  
Author(s):  
Hua Liu ◽  
Quanxin Ding ◽  
Ronggang ZHU
2000 ◽  
Vol 23 (13) ◽  
pp. 1263-1280 ◽  
Author(s):  
P. Demestichas ◽  
N. Georgantas ◽  
E. Tzifa ◽  
V. Demesticha ◽  
M. Striki ◽  
...  

2012 ◽  
Vol 21 (04) ◽  
pp. 1240015
Author(s):  
FEDOR ZHDANOV ◽  
YURI KALNISHKAN

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.


2018 ◽  
Vol 65 (6) ◽  
pp. 1359-1372 ◽  
Author(s):  
Elvira Pirondini ◽  
Behtash Babadi ◽  
Gabriel Obregon-Henao ◽  
Camilo Lamus ◽  
Wasim Q. Malik ◽  
...  

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