Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of analyses that must be conducted. Building surrogate model approximate behavior structures instead exact possible solution tackle this problem. However, existing models have been designed based on regression techniques. This paper proposes novel method, call...

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 ...

Boosting algorithms with l1-regularization are of interest because l1 regularization leads to sparser composite classifiers. Moreover, Rosset et al. have shown that for separable data, standard lpregularized loss minimization results in a margin maximizing classifier in the limit as regularization is relaxed. For the case p = 1, we extend these results by obtaining explicit convergence bounds o...

Radical extraction is the core technique for radical-based Chinese character recognition. In this paper, we proposed a new method of radical extraction – radical cascade classifier. The radical cascade classifier consists of multiple AdaBoost classifiers. It can detect and extract specific radical from characters. To apply cascade classifier to radical extraction, we focus on two main points: f...

Semantic concept classification is a critical task for content-based video retrieval. Traditional methods of machine learning focus on increasing the accuracy of classifiers or models, and face the problems of inducing new data errors and algorithm complexity. Recent researches show that fusion strategies of ensemble learning have appeared promising for improving the classification performance,...

Building a classification model from thousands of available predictor variables with a relatively small sample size presents challenges for most traditional classification algorithms. When the number of samples is much smaller than the number of predictors, there can be a multiplicity of good classification models. An ensemble classifier combines multiple single classifiers to improve classific...

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...

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...