This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it...

Boosting is a kind of ensemble methods which produce a strong learner that is capable of making very accurate predictions by combining rough and moderately inaccurate learners (which are called as base learners or weak learners). In particular, Boosting sequentially trains a series of base learners by using a base learning algorithm, where the training examples wrongly predicted by a base learn...

Ensemble learning methods have received considerable attention in the past few years. Various methods for combining several learning experts have been developed and used in different domains of machine learning. Many works have focused on decision fusion of different exports. Some methods try to train all the experts on the same training data and then use statistical techniques to combine the r...

The present paper introduces a newMultiling text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and “ensemble learning” classiers to build the best model. We have experimented our method in summary level ev...

Relational networks often evolve over time by the addition, deletion, and changing of links, nodes, and attributes. However, accurately incorporating the full range of temporal dependencies into relational learning algorithms remains a challenge. We propose a novel framework for discovering temporal-relational representations for classification. The framework considers transformations over all ...

Figure 1. A stylized depiction of how to combine the two generalizers G 1 and G 2 via stacked generalization. A learning set L is symbolically depicted by the full ellipse. We want to guess what output corresponds to the question q. To do this we create a CVPS of L; one of these partitions is shown, splitting L into {(x, y)} and {L-(x, y)}. By training both G 1 and G 2 on {L-(x, y)}, asking bot...

In the context of ensemble learning for regression problems, we study the effect of building ensembles from different model classes. Tests on real and simulated data sets show that this approach can improve model accuracy compared to ensembles from a single model class.

Recent work on multi-objectivization has shown how a single-objective reinforcement learning problem can be turned into a multi-objective problem with correlated objectives, by providing multiple reward shaping functions. The information contained in these correlated objectives can be exploited to solve the base, singleobjective problem faster and better, given techniques specifically aimed at ...