Research of machine learning methods for student management performance evaluation

2012 ◽  
Author(s):  
Xiaobo Huang ◽  
Ying Huang ◽  
Qiang Pan ◽  
Jianrong Huang ◽  
Gang Zhang
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Management Performance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Student Management

scholarly journals Performance Evaluation of Pseudo Code with Weka for Accuracy Calculation

2019 ◽  
Vol 8 (4) ◽  
pp. 7818-7823
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Data Transmission ◽  
Software Metrics ◽  
Fault Prediction ◽  
Learning Methods ◽  
Accuracy Measurement ◽  
Machine Learning Methods ◽  
Interoperability Test

Programming testing is a fundamental and essential advance of the existence cycle of programming improvement to recognize and defects in programming and afterward fix the deficiencies. The reliability of the data transmission or the quality of proper processing ,maintenance and retrieval of information to a server can be tested for some systems. Accuracy is also one factor that is usually used to the Joint Interoperability Test Command as a criterion for accessing interoperability. This is the main investigation of PC flaw forecast and exactness as per our examination, which spotlights on the utilization of PROMISE database dataset. Some PROMISE database dataset tests are compared between pseudo code (PYTHON) and actual software (WEKA),which in computer fault prediction and accuracy measurement are effective software metrics and machine learning methods.

scholarly journals Machine Learning Methods Performance Evaluation*

2021 ◽  
Vol 12 (2) ◽  
pp. 2664-2666
Author(s):  
Zakoldaev D. A., Et. al.
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Performance Evaluation ◽  
Learning Methods ◽  
Air Pollution Modeling ◽  
Machine Learning Methods ◽  
Pollution Modeling ◽  
Evaluation Approaches ◽  
Monitoring Area

In this paper, we describe an approach for air pollution modeling in the data incompleteness scenarios, when the sensors cover the monitoring area only partially. The fundamental calculus and metrics of using machine learning modeling algorithms are presented. Moreover, the assessing indicators and metrics for machine learning methods performance evaluation are described. Based on the conducted analysis, conclusions on the most appropriate evaluation approaches are made.

Performance Evaluation of Machine Learning Methods in Cultural Modeling

2009 ◽  
Vol 24 (6) ◽  
pp. 1010-1017 ◽  
Author(s):  
Xiao-Chen Li ◽  
Wen-Ji Mao ◽  
Daniel Zeng ◽  
Peng Su ◽  
Fei-Yue Wang
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Cultural Modeling
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