Object recognition using image-set or video sequence as input tends to be more robust since image-set or video sequence provides much more information than single snap-shot about the variability in the appearance of the target subject. Constrained Mutual Subspace Method (CMSM) is one of the state-of-the-art algorithms for imageset based object recognition by first projecting the image-set patte...

In this paper we i n v estigate a number of ensemble methods for improving the performance of connectionist acoustic models for large vocabulary continuous speech recognition. We discuss boosting, a data selection technique which results in an ensemble of models, and mixtures-of-experts. These techniques have been applied to multi-layer perceptron acoustic models used to build a hybrid connecti...

The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time. This paper describes a data stream classification algorith...

Recent advances in Machine Learning have brought to attention new theories of learning as well as new approaches. Among these, the Ensemble method has received wide attention and has been shown to be a promising method for classification problems. Simply speaking, the ensemble method is a learning algorithm that constructs a set of “weak” classifiers and then combines their predictions to produ...

Fuzzy cognitive maps have gained considerable research interest and widely used to analyze complex systems and making decisions. Recently they have been found large applicability in diverse domains for decision support and classification tasks. A new learning paradigm for FCMs is proposed in this research work, inheriting the main aspects of ensemble based learning approaches, such as bagging a...

Learning the global hand orientation from 2D monocular images is a challenging task, as the projected hand shape is affected by a number of variations. These include inter-person hand shape and size variations, intra-person pose and style variations and self-occlusion due to varying hand orientation. Given a hand orientation dataset containing these variations, a single regressor proves to be l...

As seen in the bibliography, Adaptive Boosting (Adaboost) is one of the most known methods to increase the performance of an ensemble of neural networks. We introduce a new method based on Adaboost where we have applied Cross-Validation to increase the diversity of the ensemble. We have used CrossValidation over the whole learning set to generate an specific training set and validation set for ...

As the current standard practice of manually recorded vital signs through a few hours is giving way to continuous, automated measurement of high resolution vital signs, it brings a tremendous opportunity to predict patient outcomes and help to improve the early care. However, making predictions in an effective way is fairly challenging, because high resolution vital signs data are multivariate,...

In this paper a novel ensemble based techniques for face recognition is presented. In ensemble learning a group of methods are employed and their results are combined to form the final results of the system. Gaining the higher accuracy rate is the main advantage of this system. Two of the most successful wrapping classification methods are bagging and boosting. In this paper we used the K neare...

Observational learning algorithm is an ensemble algorithm where each network is initially trained with a bootstrapped data set and virtual data are generated from the ensemble for training. Here we propose a modular OLA approach where the original training set is partitioned into clusters and then each network is instead trained with one of the clusters. Networks are combined with different wei...