Results 11 to 20 of about 6,722,404 (223)
Complementary Ensemble Learning
CoRR, 2021 To achieve high performance of a machine learning (ML) task, a deep learning-based model must implicitly capture the entire distribution from data. Thus, it requires a huge amount of training samples, and data are expected to fully present the real distribution, especially for high dimensional data, e.g., images, videos.
Hung Nguyen 0008, J. Morris Chang
openaire +2 more sources
Ensemble reinforcement learning: A survey
Applied Soft Computing, 2023 34 ...
Yanjie Song 0001 +6 more
openaire +5 more sources
Ensemble machine learning on gene expression data for cancer classification [PDF]
, 2003 Whole genome RNA expression studies permit systematic approaches to understanding the correlation between gene expression profiles to disease states or different developmental stages of a cell.
Tan, A C, Gilbert, D
core +6 more sources
Ensembles of Learning Machines [PDF]
, 2002 Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach ...
G. VALENTINI, MASULLI, FRANCESCO
openaire +4 more sources
Ensemble Algorithms in Reinforcement Learning [PDF]
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008 This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms.
Marco A. Wiering, Hado van Hasselt
openaire +4 more sources
Bagging ensemble selection [PDF]
, 2011 Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems.
Quan Sun +3 more
core +1 more source
, 2020 In this paper, we consider ensemble classifiers, that is, machine learning based classifiers that utilize a combination of scoring functions. We provide a framework for categorizing such classifiers, and we outline several ensemble techniques, discussing how each fits into our framework.
Mark Stamp 0001 +3 more
openaire +2 more sources
Ensemble - an E-Learning Framework [PDF]
J. Univers. Comput. Sci., 2013 JUCS - Journal of Universal Computer Science Volume Nr.
Queirós, Ricardo, Leal, José Paulo
openaire +2 more sources
Bagging ensemble selection for regression [PDF]
, 2012 Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have
Quan Sun +3 more
core +1 more source
Online learning with ensembles [PDF]
Physical Review E, 2000 Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules,
openaire +3 more sources