AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical...

The success of mobile robots relies on the ability to extract from the environment additional information beyond simple spatial relations. In particular, mobile robots need to have semantic information about the entities in the environment such as the type or the name of places or objects. This work addresses the problem of classifying places (room, corridor or doorway) using mobile robots equi...

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...