The performance of automatic speech recognition (ASR) system can be significantly enhanced with additional information from visual speech elements such as the movement of lips, tongue, and teeth, especially under noisy environment. In this paper, a novel approach for recognition of visual speech elements is presented. The approach makes use of adaptive boosting (AdaBoost) and hidden Markov mode...

In order to study the convergence properties of the AdaBoost algorithm, we reduce AdaBoost to a nonlinear iterated map and study the evolution of its weight vectors. This dynamical systems approach allows us to understand AdaBoost’s convergence properties completely in certain cases; for these cases we find stable cycles, allowing us to explicitly solve for AdaBoost’s output. Using this unusual...

Owing to the interference of the complex background in color image, high false positive rate is a problem in face detection based on AdaBoost algorithm. In addition, the training process of AdaBoost is very time consuming. To address these problems, this paper proposes a two-stage face detection method using skin color segmentation and heuristics-structured adaptive to detection AdaBoost (HAD-A...

One of the major developments in machine learning in the past decade is the Ensemble method, which finds a highly accurate classifier by combining many moderately accurate component classifiers. In this paper, we propose a classifier of integrated neuro-fuzzy system with Adaboost algorithm. It is called Hybrid-neuro-fuzzy system and Adaboost-classifier classifier. Herein, Adaboost creates a col...

AdaBoost algorithms fuse weak classifiers to be a strong classifier by adaptively determine fusion weights of weak classifiers. In this paper, an enhanced AdaBoost algorithm by adjusting inner structure of weak classifiers (ISABoost) is proposed. In the traditional AdaBoost algorithms, the weak classifiers are not changed once they are trained. In ISABoost, the inner structures of weak classifi...

Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected ar...

conclusions by comparing the results of classification using multiple classifier fusion with respect to using each classifier separately, it is found that the classifier fusion is more effective in enhancing the detection accuracy. objectives through the improvement of classification accuracy rate, this work aims to present a computer-assisted diagnosis system for malaria parasite. materials an...

AdaBoost is an iterative algorithm to constructclassifier ensembles. It quickly achieves high accuracy by focusingon objects that are difficult to classify. Because of this, AdaBoosttends to overfit when subjected to noisy datasets. We observethat this can be partially prevented with the use of validationsets, taken from the same noisy training set. But using less thanth...

There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but are more computation intensive while Adaboost ones are much faster with slightly worse performance. For possible real-time applications the Adaboost method seems a better choice. However, the existing Adaboost algorithm...

Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-toNoise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify ...