The boosting algorithm AdaBoost de veloped by Freund and Schapire has ex hibited outstanding performance on sev eral benchmark problems when using C as the weak algorithm to be boosted Like other ensemble learning approaches AdaBoost constructs a composite hy pothesis by voting many individual hy potheses In practice the large amount of memory required to store these hypotheses can make ensembl...

A common step in drug design is the formation of a quantitative structure-activity relationship (QSAR) to model an exploratory series of compounds. A QSAR generalizes how the structure of a compound relates to its biological activity. There is growing interest in the application of machine learning techniques in QSAR modeling research. However, no single technique can claim to be uniformly supe...

It is known that Boosting can be interpreted as a gradient descent technique to minimize an underlying loss function. Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers. Therefore, several Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to improve the robus...

Both empirical and mathematical demonstrations of the importance of chance-corrected measures are discussed, and a new model of learning is proposed based on empirical psychological results on association learning. Two forms of this model are developed, the Informatron as a chance-corrected Perceptron, and AdaBook as a chance-corrected AdaBoost procedure. Computational results presented show ch...

Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper we employ combination of Adaboost with Support Vector Machine (SVM) as component classifiers to be used in Face Detection Task. Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of clas...

Subjectivity analysis has received increasing attention in natural language processing field. Most of the subjectivity analysis works however are conducted on single languages. In this paper, we propose to perform multilingual subjectivity analysis by combining multi-view learning and AdaBoost techniques. We aim to show that by boosting multi-view classifiers we can develop more effective multi...

A lot of bankruptcy forecasting model has been studied. Most of them uses corporate finance data and is intended for general companies. It may not appropriate for forecasting bankruptcy of construction companies which has big liquidity. It has a different capital structure, and the model to judge the financial risk of general companies can be difficult to apply the construction companies. The e...

One of the major developments in machine learning in the past decade is the Ensemble method, which finds a highly accurate classifier by combining many moderately accurate component classifiers. In this paper, we propose a classifier of integrated neuro-fuzzy system with Adaboost algorithm. It is called Hybrid-neuro-fuzzy system and Adaboost-classifier classifier. Herein, Adaboost creates a col...

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...

The Interspeech ComParE 2014 Challenge consists of two machine learning tasks, which have quite a small number of examples. Due to our good results in ComParE 2013, we considered AdaBoost a suitable machine learning meta-algorithm for these tasks, besides we also experimented with Deep Rectifier Neural Networks. These differ from traditional neural networks in that the former have several hidde...