Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (N...

Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the result of the boosting technique reveals some inconsistent annotations in the P...

It is hard to come up with a strong learning algorithm high cross-media retrieval accuracy, but finding weak slightly higher accuracy than random prediction simple. This paper proposes an innovative Bagging-based (called BCMR) based on this concept. First, we use bootstrap sampling take sample from the original set. The amount of abstracted by bootstrapping set be same as dataset. Secondly, 50 ...

Bagging [1] is a technique that tries to improve a learning algorithm's performance by using bootstrap replicates of the training set [5, 4]. The computational requirements for estimating the resultant generalization error on a test set by means of cross-validation are often prohibitive for leave-one-out cross-validation one needs to train the underlying algorithm on the order of m times, where...

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors...

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning. Three alternative ensemble strategies for bin...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.04.054 * Corresponding author. E-mail addresses: [email protected] (D. Zhan Zhou), [email protected] (S.C.H. Leung). In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. The...