Ensemble learning has been widely employed by mobile applications, ranging from environmental sensing to activity recognitions. One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning. During crowdsourcing, the centralized aggregator releases ensemble learning models to a large number of mo...

Ensemble learning methods train combinations of base models, which may be decision trees, neural networks, or others traditionally used in supervised learning. Ensemble methods have gained popularity because many researchers have demonstrated their superior prediction performance relative to single models on a variety of problems especially when the correlations of the errors made by the base m...

We develop a Bayesian “sum-of-trees” model, named BART, where each tree is constrained by a prior to be a weak learner. Fitting and inference are accomplished via an iterative backfitting MCMC algorithm. This model is motivated by ensemble methods in general, and boosting algorithms in particular. Like boosting, each weak learner (i.e., each weak tree) contributes a small amount to the overall ...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

Abstract: Along with the increase of data usage in actual applications, it has become an important issue in ensemble learning to improve the ability for data analysis and processing. In order to improve the learning precision and get more accurate classification and projections in practical problems, triangular norms are introduced into the ensemble learning system. Triangular norms can improve...

Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the generalization ability of such individual learner can ...

In recent years, semi-supervised learning has been a hot research topic in machine learning area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each example is represented by two or more r...

Ensemble learning has gained success in machine with major advantages over other methods. Bagging is a prominent ensemble method that creates subgroups of data, known as bags, are trained by individual methods such decision trees. Random forest example bagging additional features the process. Evolutionary algorithms have been for optimisation problems and also used learning. gradient-free work ...

AbstractTo accelerate learning process with few samples, meta-learning resorts to prior knowledge from previous tasks. However, the inconsistent task distribution and heterogeneity is hard be handled through a global sharing model initialization. In this paper, based on gradient-based meta-learning, we propose an ensemble embedded algorithm (EEML) that explicitly utilizes multi-model-ensemble o...