We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel machines. Special cases considered are bagging and support vector machines. We present experimental results supporting the theoretical bounds, and describe characteristics of kernel machines ensembles suggested from the ...

In this paper we study the stability of support vector machines in face detection by decomposing their average prediction error into the bias, variance, and aggregation effect terms. Such an analysis indicates whether bagging, a method for generating multiple versions of a classifier from bootstrap samples of a training set, and combining their outcomes by majority voting, is expected to improv...

Auditor appointment can be regarded as a matter of pursued audit quality and is driven by several factors. The adoption of an effective auditor procurement process increases the likelihood that a company will engage the right auditor at a fair price. In this study, three techniques derived from artifi cial intelligence (AI) are used to propose models capable of discriminating between cases wher...

Uncertainty representation is a major issue in pattern recognition. In many applications, the outputs of a classifier do not lead directly to a final decision, but are used in combination with other systems, or as input to an interactive decision process. In such contexts, it may be advantageous to resort to rich and flexible formalisms for representing and manipulating uncertain information. T...

Imbalanced class problems appear in many real applications of classification learning. We propose a novel sampling method to improve bagging for data sets with skewed class distributions. In our new sampling method “Roughly Balanced Bagging” (RB Bagging), the number of samples in the largest and smallest classes are different, but they are effectively balanced when averaged over all subsets, wh...

Several potential network structures are chosen to do a large number of experimental analysis, historical data is divided into training sample and testing sample, and the corresponding neural network model is established with BP learning algorithm. After checking the testing sample, a superior network integration model which can be applied for hydraulic metal structure health grade diagnosing i...

Understanding influence plays a vital role in enhancing businesses operation and improving effect of information propagation. Therefore the user influence in social media, such as Twitter, is widely studied based on different standards, such as the number of followers, retweets and so on. However, little work considers the accurate click number of short URLs as the measurement of influence. In ...

Adjustment for covariates is a time-honored tool in statistical analysis and is often implemented by including the covariates that one intends to adjust as additional predictors in a model. This adjustment often does not work well when the underlying model is misspecified. We consider here the situation where we compare a response between two groups. This response may depend on a covariate for ...

In this paper, experiments on various classifiers and combining these classifiers are done, reported and analyzed. Combining the classifiers means having the single classifiers support each other in making a decision, instead of having only a single classifier’s decision as the final decision. The base experiment involves, both, applying different single classifiers on a dataset and applying th...

Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that le...