Real Adaboost ensembles with weighted emphasis (RA-we) on erroneous and critical (near the classification boundary) samples have recently been proposed, leading to improved performance when an adequate combination of these terms is selected. However, finding the optimal emphasis adjustment is not an easy task. In this paper, we propose to make a fusion of the outputs of RA-we ensembles trained ...

This paper introduces a visual zebra crossing detector based on the Viola-Jones approach. The basic properties of this cascaded classifier and the use of integral images are explained. Additional preand postprocessing for this task are introduced and evaluated.

Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning a...

We congratulate the authors for their interesting papers on boosting and related topics. Jiang deals with the asymptotic consistency of Adaboost. Lugosi and Vayatis study the convex optimization of loss functions associated with boosting. Zhang studies the loss functions themselves. Their results imply that boosting-like methods can reasonably be expected to converge to Bayes classifiers under ...

Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too “dull” to handle such imbalanced classification. In this paper, we improve dete...

In this paper we apply multi-armed bandits (MABs) to improve the computational complexity of AdaBoost. AdaBoost constructs a strong classifier in a stepwise fashion by selecting simple base classifiers and using their weighted “vote” to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs where each arm ...

AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear combination. It has been observed that the generalization error of the algorithm continues to improve even after all examples are classified correctly by the current signed linear combination, which can be viewed as hyperplane in feature space where the base hypotheses form the features. The impr...

Considering the problem how to protect the cloud services from being destroyed by cloud users, the riskprediction model based on improved AdaBoost method is proposed. The risk prediction is regarded as two-class classification problem, and the risk of new cloud users could be predicted by the attributes of historical cloud users. In order to improve the result of predicted, AdaBoost method is a...

In this paper, some chemometrics methods have been applied for modeling and predicting classification of Estrogen Receptor-β (ERβ) selective ligands derivatives with radial distribution function (RDF) descriptor calculated from the molecular structure alone for the first time. The particle swarm optimization (PSO) and genetic algorithms (GA) methods have been used to select descriptors which ar...

In the last decade, one of the research topics that has received a great deal of attention from the machine learning and computational learning communities has been the so called boosting techniques. In this paper, we further explore this topic by proposing a new boosting algorithm that mends some of the problems that have been detected in the, so far most successful boosting algorithm, AdaBoos...