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...

Stability has been explored to study the performance of learning algorithms in recent years and it has been shown that stability is sufficient for generalization and is sufficient and necessary for consistency of ERM in the general learning setting. Previous studies showed that AdaBoost has almost-everywhere uniform stability if the base learner has L1 stability. The L1 stability, however, is t...

Boosting and bagging are two techniques for improving the performance of learning algorithms. Both techniques have been successfully used in machine learning to improve the performance of classification algorithms such as decision trees, neural networks. In this paper, we focus on the use of feedforward back propagation neural networks for time series classification problems. We apply boosting ...

Modulation scheme recognition occupies a crucial position in the civil and military application. In this paper, we present boosting algorithm as an ensemble frame to achieve a higher accuracy than a single classifier. To evaluate the effect of boosting algorithm, eight common communication signals are yet to be identified. And five kinds of entropy are extracted as the training vector. And then...

We propose a general framework for analyzing and developing fully corrective boosting-based classifiers. The framework accepts any convex objective function, and allows any convex (for example, ℓp-norm, p ≥ 1) regularization term. By placing the wide variety of existing fully corrective boosting-based classifiers on a common footing, and considering the primal and dual problems together, the fr...

In this article, we discuss a class of stochastic boosting algorithms, which corrects and develops the work of [23], showing how to perform statistical inference in a computationally efficient manner. Sequential Monte Carlo (SMC) methods are used to illustrate that the stochastic boosting methods can provide better predictions, for a higher computational cost, than the corresponding boosting al...

Based on data from ongoing and completed studies, and to the extent the sponsors have provided requested information, TAG and the undersigned organizations and patient advocates of the community of people living with HIV/AIDS support the full approval of the GSK/Vertex application to FDA of Lexiva® brand 908 (fosamprenavir, NDA 21-XXX) to treat HIV infection in combination with other antiretrov...

We propose an ensemble learning method called Network Boosting which combines weak learners together based on a random graph (network). A theoretic analysis based on the game theory shows that the algorithm can learn the target hypothesis asymptotically. The comparison results using several datasets of the UCI machine learning repository and synthetic data are promising and show that Network Bo...

We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for this problem (BenDavid et al., 2001; Gavinsky, 2002; Kalai et al. , 2008) follow the same strategy as boosting algorithms in the PAC model: the weak learner is executed on the same target function but over different dis...

We discuss algorithmic aspects of boosting techniques, such as Majority Vote Boosting [Fre95], AdaBoost [FS97], and MadaBoost [DW00a]. Considering a situation where we are given a huge amount of examples and asked to find some rule for explaining these example data, we show some reasonable algorithmic approaches for dealing with such a huge dataset by boosting techniques. Through this example, ...