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...

As seen in the bibliography, Adaptive Boosting (Adaboost) is one of the most known methods to increase the performance of an ensemble of neural networks. We introduce a new method based on Adaboost where we have applied Cross-Validation to increase the diversity of the ensemble. We have used CrossValidation over the whole learning set to generate an specific training set and validation set for ...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of Margins" (DOOM) algorithm. The dark curve is AdaBoost, the light curve is DOOM. DOOM sacrifices significant training error for improved test error (horizontal marks on margin= 0 line)_ -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Margin

Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can b...