Force spectroscopy of individual DNA and RNA molecules provides unique insights into the structure and mechanics of these for life so essential molecules. Observations of DNA and RNA molecules one at a time provide spatial, structural, and temporal information that is complementary to the information obtained by classical ensemble methods. Single-molecule force spectroscopy has been realized on...

Scalable Ensemble Learning and Computationally Efficient Variance Estimation

We investigate ensemble learning methods for hybrid music recommenders, combining a social and a content-based recommender algorithm in an initial experiment by applying a simple combination rule to merge recommender results. A first experiment suggests that such a combination can reduce the mean absolute prediction error compared to the used recommenders’ individual errors.

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show that using ensemble approaches can dra...

AdaBoost !5] is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step ill AdaBoost is constructing a distribution over the training examples to crette each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by tLe previous base model in the sequence [6]. The idea is to m...

This paper uses neural network models and the ensemble technique of stacked generalization to investigate the relative importance of situational and demographic factors on consumer choice. We discuss the theoretical justification for this approach and develop the level-0 and level-1 models which we estimate using a consumer choice data set from AT&T. The findings confirm the superiority of situ...

Recently many statistical learning techniques are successfully developed and used in several areas. However, these algorithms sometimes are not robust and does not show good performances. The ensemble method can solve these problems. It is known that the ensemble learning sometimes improves the generalized performance of machine learning tasks as well as makes it robust. However, the combining ...

The question why deep learning algorithms perform so well in practice has puzzled machine learning theoreticians and practitioners alike. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce ...