Boosting is a technique of combining a set weak classifiers to form one high-performance prediction rule. Boosting was successfully applied to solve the problems of object detection, text analysis, data mining and etc. The most and widely used boosting algorithm is AdaBoost and its later more effective variations Gentle and Real AdaBoost. In this article we propose a new boosting algorithm, whi...

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...

Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. However, important questions were raised about the margin explanation. Breiman (1999) proved a bound ...

Segmentation plays a vital role in determining the tumor in brain MR Images. The analysis is done using multifractional Brownian motion (mBm) to devise the tumor in brain MR images. The spatially varying feature is extracted using mBm and corresponding algorithm. Then segmentation is carried out based on multifractal features. An algorithm for segmentation is proposed by modifying the well-know...

AdaBoost can be derived by sequential minimization of the exponential loss function. It implements the learning process by exponentially reweighting examples according to classification results. However, weights are often too sharply tuned, so that AdaBoost suffers from the nonrobustness and overlearning. Wepropose a new boosting method that is a slight modification of AdaBoost. The loss functi...

We provide an analysis of AdaBoost within the framework of algorithmic stability. In particular, we show that AdaBoost is a stabilitypreserving operation: if the “input” (the weak learner) to AdaBoost is stable, then the “output” (the strong learner) is almost-everywhere stable. Because classifier combination schemes such as AdaBoost have greatest effect when the weak learner is weak, we discus...

This paper presents a novel method of detecting faces at any degree of rotation in the image plane based on CostSensitive AdaBoost (CS-AdaBoost) algorithm. The method first employs a cascade of very simple classifiers trained by CS-AdaBoost to determine the possible orientation of each input window and then uses an upright face detector also trained by CS-AdaBoost to verify the derotated face c...

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...

A key challenge in computer vision applications is detecting objects in an image which is a non-trivial problem. One of the better performing proposed algorithms falls within the Viola and Jones framework. They make use of Adaboost for training a cascade of classifiers. The challenges of Adaboost-based face detector include the selection of the most relevant features which are considered as wea...

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...