The Naïve Bayesian Classifier Learning Algorithm Based on Adaboost and Parameter Expectations

2010 ◽  
Author(s):  
Hongbo Shi ◽  
Xiaoyong Lv
Keyword(s):  
Learning Algorithm ◽  
Bayesian Classifier ◽  
Classifier Learning

scholarly journals A Bayesian Classifier Learning Algorithm Based on Optimization Model

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sanyang Liu ◽  
Mingmin Zhu ◽  
Youlong Yang
Keyword(s):  
Objective Function ◽  
Optimization Model ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Bayesian Classifier ◽  
Bayes Classifier ◽  
Independence Assumption ◽  
Maximum Value ◽  
Chi Squared ◽  
Classifier Learning

Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes and affects its classification performance. In this paper, we summarize the existing improved algorithms and propose a Bayesian classifier learning algorithm based on optimization model (BC-OM). BC-OM uses the chi-squared statistic to estimate the dependence coefficients among attributes, with which it constructs the objective function as an overall measure of the dependence for a classifier structure. Therefore, a problem of searching for an optimal classifier can be turned into finding the maximum value of the objective function in feasible fields. In addition, we have proved the existence and uniqueness of the numerical solution. BC-OM offers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.

An Algorithm of Incremental Bayesian Classifier Based on K-Nearest Neighbor

2011 ◽  
Vol 403-408 ◽  
pp. 1455-1459
Author(s):  
Dong Wang ◽  
Shi Huan Xiong
Keyword(s):  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Bayesian Classifier ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Learning Sequence ◽  
Similar Distance ◽  
Class Weight ◽  
Class Information ◽  
And Training

The learning sequence is an important factor of affecting the study effect about incremental Bayesian classifier. Reasonable learning sequence helps to strengthen the knowledge reserve of the classifier. This article proposes an incremental learning algorithm based on the K-Nearest Neighbor. Through calculating k maximum similar distance between test set and training set ,dividing and structuring the sequence of class number and the sequence of sum of class weight. According to the undulation degree of sequence, the instance including stronger class information is chosen to enter the learning process firstly. The experimental result indicates that the algorithm is effective and feasible.

An Application Research on Adaptive Learning Algorithm in Image Recognition

2014 ◽  
Vol 926-930 ◽  
pp. 2947-2950
Author(s):  
Hai Long Jia ◽  
Kun Cao
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Adaptive Resonance Theory ◽  
Application Research ◽  
Resonance Theory ◽  
Diagnostic Image ◽  
Adaptive Resonance ◽  
Classifier Learning

This paper studies adaptive learning for diagnostic image recognition and expounds that adaptive resonance theory is utilized to achieve ART artificial neural network of self-stability and self-makeup for recognition, which meets the requirement of learning and adaption. In terms of the principle, an algorithm of self-stability and classifier learning is also provided.

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