With the development of image processing technology and popularization of computer technology, intelligent machine vision technology has a wide range of application in the medical, military, industrial and other fields. Target tracking feature selection algorithm is one of research focuses in the machine intelligent vision technology. Therefore, to design the target tracking feature selection a...

Recent experiments and theoretical studies show that AdaBoost can over t in the limit of large time. If running the algorithm forever is suboptimal, a natural question is how low can the prediction error be during the process of AdaBoost? We show under general regularity conditions that during the process of AdaBoost a consistent prediction is generated, which has the prediction error approxima...

In this paper, we propose an approach which improves the accuracy of detecting phishing sites by employing the AdaBoost algorithm. Although there are heuristics to detect phishing sites, existing anti-phishing tools still do not achieve high accuracy in detection. We hypothesize that the inaccuracy is caused by anti-phishing tools that can not use these heuristics appropriately. Our attempt is ...

The boosting algorithm AdaBoost de veloped by Freund and Schapire has ex hibited outstanding performance on sev eral benchmark problems when using C as the weak algorithm to be boosted Like other ensemble learning approaches AdaBoost constructs a composite hy pothesis by voting many individual hy potheses In practice the large amount of memory required to store these hypotheses can make ensembl...

We propose to use the AdaBoost algorithm for face recognition. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face recognition, the original Adaboost which uses all given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for ...

A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the...

Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper we employ combination of Adaboost with Support Vector Machine (SVM) as component classifiers to be used in Face Detection Task. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of clas...

My final project was to implement and compare a number of Naive Bayes and boosting algorithms. For this task I chose to implement two Naive Bayes algorithms that are able to make use of binary attributes, the multivariate Naive Bayes and the multinomial Naive Bayes with binary attributes. For the boosting side of the algorithms I chose to implement AdaBoost, and its close bother AdaBoost*. Both...

Subjectivity analysis has received increasing attention in natural language processing field. Most of the subjectivity analysis works however are conducted on single languages. In this paper, we propose to perform multilingual subjectivity analysis by combining multi-view learning and AdaBoost techniques. We aim to show that by boosting multi-view classifiers we can develop more effective multi...