Q-Learning with Fisher Score for Feature Selection of Large-Scale Data Sets

Author(s):  
Min Gan ◽  
Li Zhang
2016 ◽  
Vol 329 ◽  
pp. 690-700 ◽  
Author(s):  
Fachao Li ◽  
Zan Zhang ◽  
Chenxia Jin

Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.


2017 ◽  
Author(s):  
Shirley M. Matteson ◽  
Sonya E. Sherrod ◽  
Sevket Ceyhun Cetin

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