Results 41 to 50 of about 914,879 (290)
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
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Gradient boosting with extreme-value theory for wildfire prediction [PDF]
, 2022 This paper details the approach of the team $\textit{Kohrrelation}$ in the 2021 Extreme Value Analysis data challenge, dealing with the prediction of wildfire counts and sizes over the contiguous US. Our approach uses ideas from extreme-value theory in a
Koh, Jonathan, Jonathan Koh
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Gradient tree boosting for hierarchical data
, 2022 This OSF project contains the R files containing the gradient tree boosting, MERT boosting and REEM boosting functions as well as the two simulation scripts and the data and analysis file for the illustrative data ...
Steffen Nestler +2 more
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Machine Learning-Based Forecasting of Bitcoin Price Movements
Proceedings of the International Conference on Applied Innovations in ITIn the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks,
Darko Angelovski +4 more
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Sequential Training of Neural Networks With Gradient Boosting
IEEE Access, 2023 This paper presents a novel technique based on gradient boosting to train the final layers of a neural network (NN). Gradient boosting is an additive expansion algorithm in which a series of models are trained sequentially to approximate a given function.
Seyedsaman Emami, Gonzalo Martinez-Munoz
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International Journal of Machine Learning and CyberneticsAbstract This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration has to be trained.
Seyedsaman Emami +1 more
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Boosting Ridge Regression [PDF]
, 2005 Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
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A new formulation of gradient boosting
Machine Learning: Science and Technology, 2021 Abstract In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the ...
Alex Wozniakowski +3 more
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Soft Gradient Boosting Machine
CoRR, 2020 Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base learner has to take the sequential order, it is infeasible to parallelize the training process among base learners for speed-up.
Ji Feng +3 more
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Comparing ensemble learning algorithms and severity of illness scoring systems in cardiac intensive care units: a retrospective study [PDF]
Einstein (São Paulo)Objective: Logistic Regression has been used traditionally for the development of most predictor tools of intensive care unit mortality. The purpose of this study is to combine shared risk factors between patients undergoing cardiac surgery and intensive
Beatriz Nistal-Nuño
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