We saw last time that the training error of AdaBoost decreases exponentially as the number of rounds T grows. However, this says nothing about how well the function output by AdaBoost performs on new examples. Today we will discuss the generalization error of AdaBoost. We know that AdaBoost gives us a consistent function quickly; the bound we derived on training error decreases exponentially, a...

Detecting anatomical structures, such as the carina, the pulmonary trunk and the aortic arch, is an important step in designing a CAD system of detection Pulmonary Embolism. The presented CAD system gets rid of the high-level prior defined knowledge to become a system which can easily extend to detect other anatomic structures. The system is based on a machine learning algorithm — AdaBoost and ...

Three AdaBoost variants are distinguished based on the strategies applied to update the weights for each new ensemble member. The classic AdaBoost due to Freund and Schapire only decreases the weights of the correctly classified objects and is conservative in this sense. All the weights are then updated through a normalization step. Other AdaBoost variants in the literature update all the weigh...

Adaboost is an ensemble learning algorithm that combines many other learning algorithms to improve their performance. Starting with Viola and Jones’ researches [14][15], Adaboost has often been used to local-feature selection for object detection. Adaboost by ViolaJones consists of following two optimization schemes: (1) parameter fitting of local features, and (2) selection of the best local f...

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...

AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present ...

AdaBoost has been verified to be proficient in processing images rapidly while attaining high detection rate in face detection. The speed of AdaBoost in face detection is demonstrated in [1], where the detection can be performed in 15 frames per second. The robust speediness and the high accuracy in tracing the target objects have enable AdaBoost to be successful in classification problems. In ...

Boosting is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and inaccurate rules. The AdaBoost algorithm of Freund and Schapire was the first practical boosting algorithm, and remains one of the most widely used and studied, with applications in numerous fields. This chapter aims to review some of the many perspec...

We propse a new boosting algorithm that mends some of the problems that have been detected in the so far most successful boosting algorithm, AdaBoost due to Freund and Schapire [FS97]. These problems are: (1) AdaBoost cannot be used in the boosting by filtering framework, and (2) AdaBoost does not seem to be noise resistant. In order to solve them, we propose a new boosting algorithm MadaBoost ...

For color images in a complex background, we cannot be able to detect faces quickly. So we put forward an algorithm, which is based on skin color feature and the improved AdaBoost algorithm. First, through the skin color detection to excluding large amounts of complex background of non-face, after that define the face candidate regions. Besides, when the image is darkness, we will increase the ...