Results 11 to 20 of about 914,879 (290)
Optimization by Gradient Boosting [PDF]
, 2021 Gradient boosting is a state-of-the-art prediction technique that sequentially produces a model in the form of linear combinations of simple predictors---typically decision trees---by solving an infinite-dimensional convex optimization problem. We provide in the present paper a thorough analysis of two widespread versions of gradient boosting, and ...
Biau, Gérard, Cadre, Benoît
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CoRR, 2015 We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online ...
Alina Beygelzimer +3 more
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Wavelet-based gradient boosting
Statistics and Computing, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eugene Dubossarsky +3 more
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, 2021 Tiivistelmä. Tässä tutkielmassa käsitellään Gradient boosting algoritmia. Algoritmi käsittelee ohjatun oppimisen menetelmin läpi dataa ja pyrkii tekemään tämän avulla luotettavia ennusteita. Tutkielmassa käydään läpi yleisesti ohjattua oppimista ja päätöspuita sekä verrataan mahdollisia muita samankaltaisia menetelmiä.
Polviander, Oona
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Gradient-Free Gradient Boosting [PDF]
, 2020 Motivated by applications in fraud detection, this dissertation is concerned about model selection in predictive models where the correct ranking of observations has to be predicted. For this, the thesis starts by proving the asymptotic linearity of a whole family of regularized M-estimators which covers for example the Lasso.
Werner, Tino
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Randomized Gradient Boosting Machine [PDF]
SIAM Journal on Optimization, 2020 The Gradient Boosting Machine (GBM) introduced by Friedman [J. H. Friedman, Ann. Statist., 29 (2001), pp. 1189--1232] is a powerful supervised learning algorithm that is very widely used in practice---it routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup.
Haihao Lu, Rahul Mazumder
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A meta-learning based stacked regression approach for customer lifetime value prediction
Journal of Economy and Technology, 2023 Companies across the globe are keen on targeting potential high-value customers in an attempt to expand revenue, and this could be achieved only by understanding the customers more.
Karan Gadgil +2 more
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Gradient boosted feature selection [PDF]
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure.
Zhixiang Eddie Xu +3 more
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CoRR, 2018 6 pages, 1 figure, ICML 2018 AutoML ...
Janek Thomas, Stefan Coors, Bernd Bischl
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Gradient boosting to boost the efficiency of hydraulic fracturing [PDF]
Journal of Petroleum Exploration and Production Technology, 2019 In this paper, we present a data-driven model for forecasting the production increase after hydraulic fracturing (HF). We use data from fracturing jobs performed at one of the Siberian oilfields. The data includes features, characterizing the jobs, and geological information.
Ivan Makhotin +2 more
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