This paper proposes a learning scheme based still image super-resolution reconstruction algorithm. Superresolution reconstruction is proposed as a binary classification problem and can be solved by conditional class probability estimation. Assuming the probability takes the form of additive logistic regression function, AdaBoost algorithm is used to predict the probability. Experiments on face ...

We present MBoost, a novel extension to AdaBoost that extends boosting to use multiple weak learners explicitly, and provides robustness to learning models that overfit or are poorly matched to data. We demonstrate MBoost on a variety of problems and compare it to cross validation for model selection.

Boosting is a general method for improving the accuracy of any given learning algorithm. This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting’s relationship to support-vector machines. Some examples of recent applications of boostin...

Multiple Instance Learning Via Embedded Instance Selection (MILES) has shown good performance in dealing with noisy training samples, but its bag prediction rule may introduce new uncertainty into the remote sensing image classification results. In order to overcome this limitation, two popular ensemble learning strategies, Bagging and AdaBoost are integrated with MILES. Two methods are propose...

This paper shows that the region classification task can benefit from instance-transfer learning. It proposes to implement a standard region-classification algorithm using a general nonconformity function based on the Transfer AdaBoost algorithm. The experiments show that the new approach produces valid class regions when instances are transferred from a close domain. The conditions for success...

We present a package for R language containing a set of tools for regression using ensembles of learning machines and for time series forecasting. The package contains implementations of Bagging and Adaboost for regression, and algorithms for computing mutual information, autocorrelation and false nearest neighbors.

Boosting is a general method for improving the accuracy of any given learning algorithm. This short paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suuer from overrtting. Some examples of recent applications of boosting are also described.

We report on work that is part of the development of an agentbased structural health monitoring system. The data used are acoustic emission signals, and we classify these signals according to source mechanisms. The agents are proxies for communicationand computation-intensive techniques and respond to the situation at hand by determining an appropriate constellation of techniques. It is critica...

In an earlier paper [9], we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a “pseudo-loss” which is a method for forcing a learning algorithm of multi-label...

Owing to the interference of the complex background in color image, high false positive rate is a problem in face detection based on AdaBoost algorithm. In addition, the training process of AdaBoost is very time consuming. To address these problems, this paper proposes a two-stage face detection method using skin color segmentation and heuristics-structured adaptive to detection AdaBoost (HAD-A...