
Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning
In this paper, we propose a gradient boosting algorithm called adaptive ...
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Histogram Transform Ensembles for Largescale Regression
We propose a novel algorithm for largescale regression problems named h...
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GBHT: Gradient Boosting Histogram Transform for Density Estimation
In this paper, we propose a density estimation algorithm called Gradient...
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Stochastic Configuration Networks Ensemble for LargeScale Data Analytics
This paper presents a fast decorrelated neuroensemble with heterogeneou...
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Almost sure convergence of the accelerated weight histogram algorithm
The accelerated weight histogram (AWH) algorithm is an iterative extende...
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Rescale boosting for regression and classification
Boosting is a learning scheme that combines weak prediction rules to pro...
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Kernelbased L_2Boosting with Structure Constraints
Developing efficient kernel methods for regression is very popular in th...
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Gradient Boosted Binary Histogram Ensemble for Largescale Regression
In this paper, we propose a gradient boosting algorithm for largescale regression problems called Gradient Boosted Binary Histogram Ensemble (GBBHE) based on binary histogram partition and ensemble learning. From the theoretical perspective, by assuming the Hölder continuity of the target function, we establish the statistical convergence rate of GBBHE in the space C^0,α and C^1,0, where a lower bound of the convergence rate for the base learner demonstrates the advantage of boosting. Moreover, in the space C^1,0, we prove that the number of iterations to achieve the fast convergence rate can be reduced by using ensemble regressor as the base learner, which improves the computational efficiency. In the experiments, compared with other stateoftheart algorithms such as gradient boosted regression tree (GBRT), Breiman's forest, and kernelbased methods, our GBBHE algorithm shows promising performance with less running time on largescale datasets.
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