نتایج جستجو برای: ensemble learning
تعداد نتایج: 635149 فیلتر نتایج به سال:
We consider the combination of the outputs of several classifiers trained independently for the same discrimination task. We introduce new results which provide optimal solutions in the case of linear combinations. We compare our solutions to existing ensemble methods and characterize situations where our approach should be preferred.
Article history: Received 27 August 2012 Received in revised form 1 August 2013 Accepted 5 August 2013 Available online 15 August 2013
Abstract here...
Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets. Our empirical study shows that our me...
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...
Process-based modeling is an approach to learning understandable, explanatory models of dynamic systems from domain knowledge and data. Although their utility has been proven on many tasks of modeling dynamic systems in various domains, their ability to accurately predict the future behavior of an observed system is limited. To address this limitation, we propose the use of a standard approach ...
The nite-sample as well as the asymptotic distribution of Leung and Barrons (2006) model averaging estimator are derived in the context of a linear regression model. An impossibility result regarding the estimation of the nite-sample distribution of the model averaging estimator is obtained.
Several ensemble methods have been proposed that can accommodate differing base model types. This document reviews the recent literature, and for each method, we identify (1) main contributions, (2) theoretical motivation, (3) empirical results and (4) relationships to other techniques.
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