Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep architectures are showing performance compared the shallow or traditional models. Deep ensemble combine advantages of both as well such that final model has This paper reviews state-of-art and hence serves an extensive summary for researchers. The broadly categorized into bagging, b...

The standard method for training Hidden Markov Models optimizes a point estimate of the model parameters. This estimate, which can be viewed as the maximum of a posterior probability density over the model parameters, may be susceptible to over-tting, and contains no indication of parameter uncertainty. Also, this maximummay be unrepresentative of the posterior probability distribution. In this...

Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor. Bagging is one of the suitable ensemble learning methods. Ensemble learning is a simple, useful and effective metaclassification methodology that combines the predictions from multiple base classifiers (or learners). In this paper we show a comparative study of different classifiers (Dec...

Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approac...

Neural network ensemble learning has been turned out to be an efficient strategy for achieving high performance, especially in fields where the development of a powerful single learning system requires considerable efforts (Lai et al., 2006, Yu et al., 2007, 2008b). Usually, neural network ensemble learning model outperforms the individual neural network models, whose performance is limited by ...

Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the...