Cascades of classifiers constitute an important architecture for fast object detection. While boosting of simple (weak) classifiers provides an established framework, the design of similar architectures with more powerful (strong) classifiers has become the subject of current research. In this paper, we focus on greedy strategies recently proposed in the literature that allow to learn sparse Su...

Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important...

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...

This project explores recognition and classification of objects in images using the popular and effective machine learning method, Boosting. Boosting provides a framework for developing robust object detection algorithms. Boosting itself involves the training of a series of increasingly discriminating simple classifiers, and then blending their outputs – further, it involves constructing a clas...

Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep residual network (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct T weak module classifiers, each contains two of the T layers, such that the combined strong learner is a ResNet. Therefore,...

A reinforced AdaBoost learning algorithm is proposed for object detection with local pattern representations. In implementing AdaBoost learning, the proposed algorithm employs an exponential criterion as a cost function and Newton's method for its optimization. In particular, we introduce an optimal selection of weak classifiers minimizing the cost function and derive the reinforced predictions...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

Large number of applications involving multiple views of data are coming into use, e.g., reporting news on the Internet by both text and video, identifying a person by both fingerprints and face images, etc. Meanwhile, labeling these data needs expensive efforts and thus most data are left unlabeled in many applications. Co-training can exploit the information of unlabeled data in multi-view sc...

We propose a generic framework to handle missing weak classifiers at testing stage in a boosted cascade. The main contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: (1) the approximation of posterior probabilities on each level and (2) the computation of threshold...