Margin-maximizing techniques such as boosting have been generating excitement in machine learning circles for several years now. Although these techniques offer significant improvements over previous methods on classification tasks, little research has examined the application of techniques such as boosting to the problem of retrieval from image and video databases. This paper looks at boosting...

Boosting is a generic learning method for classification and regression. Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance. Overfitting is an important and ubiquitous phenomenon, especially in regression settings. To avoid overfitting, we consider using l1 regularization. We propose a novel Frank-Wolfe type boosting algorithm (FWBoost...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm , we brieey survey theoretical work on boosting including analyses of AdaBoost's training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass c...

Classiier committee learning approaches have demonstrated great success in increasing the prediction accuracy of classiier learning , which is a key technique for datamining. It has been shown that Boosting and Bagging, as two representative methods of this type, can signiicantly decrease the error rate of decision tree learning. Boosting is generally more accurate than Bagging, but the former ...

In many problem domains, combining the predictions of several models often results in a model with improved predictive performance. Boosting is one such method that has shown great promise. On the applied side, empirical studies have shown that combining models using boosting methods produces more accurate classification and regression models. These methods are extendible to the exponential fam...

In this paper, a new algorithm called Averaged Conservative Boosting (ACB) is presented to build ensembles of neural networks. In ACB we mix the improvements that Averaged Boosting (Aveboost) and Conservative Boosting (Conserboost) made to Adaptive Boosting (Adaboost). In the algorithm we propose we have applied the conservative equation used in Conserboost along with the averaged procedure use...

Boosting [6,9,12,15,16] is an extremely successful and popular supervised learning technique that combines multiple “weak” learners into a more powerful “committee.” AdaBoost [7, 12, 16], developed in the context of classification, is one of the earliest and most influential boosting algorithms. In our paper [5], we analyze boosting algorithms in linear regression [3,8,9] from the perspective o...