Constructing Weak Learner and Performance Evaluation in AdaBoost

Author

Zhou, Mian ; Wei, Hong

Author_Institution

Dept. of Educ. Technol., Tianjin Foreign Studies Univ., Tianjin, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

This paper gives a deep investigation into AdaBoost algorithm, which is used to boost the performance of any given learning algorithm. Within AdaBoost, weak learners are crucial and primitive parts of the algorithm. Since weak learners are required to train with weights, two types of weak learners: artificial neural network weak learner and naive Bayes weak learner are designed. The results show AdaBoost by naive Bayes weak learners is superior to artificial neural network weak learners, it shares the same generalisation ability with support vector machine.

Keywords

Bayes methods; learning (artificial intelligence); neural nets; support vector machines; AdaBoost; artificial neural network weak learner; learning algorithm; naive Bayes weak learner; support vector machine; Artificial neural networks; Boosting; Iris; Machine learning; Machine learning algorithms; Optical character recognition software; Optical noise; Probability distribution; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5362581

Filename

5362581