Multi-threshold Ranking Model Based on Naive Bayes for Community-Ranked Article Submission Sites

2010 ◽  
Author(s):  
Nianzu Liu ◽  
Guanglin Xu ◽  
Yongchang Liu
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Model Based ◽  
Ranking Model

scholarly journals Construction of Music Teaching Evaluation Model Based on Weighted Naïve Bayes

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiongjun Xia ◽  
Jin Yan
Keyword(s):  
Evaluation System ◽  
Naive Bayes ◽  
Evaluation Model ◽  
Audio Signal ◽  
Naïve Bayes ◽  
Teaching Evaluation ◽  
Machine Learning Algorithms ◽  
Music Teaching ◽  
Model Based ◽  
Bayes Algorithm

Evaluation of music teaching is a highly subjective task often depending upon experts to assess both the technical and artistic characteristics of performance from the audio signal. This article explores the task of building computational models for evaluating music teaching using machine learning algorithms. As one of the widely used methods to build classifiers, the Naïve Bayes algorithm has become one of the most popular music teaching evaluation methods because of its strong prior knowledge, learning features, and high classification performance. In this article, we propose a music teaching evaluation model based on the weighted Naïve Bayes algorithm. Moreover, a weighted Bayesian classification incremental learning approach is employed to improve the efficiency of the music teaching evaluation system. Experimental results show that the algorithm proposed in this paper is superior to other algorithms in the context of music teaching evaluation.

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