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Accelerated Gradient Boosting [PDF]
Machine Learning, 2019 Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov's accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting).
Gérard Biau
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Lecture Notes in Computer Science, 2023 Gradient Boosting Machines (GBMs) have revealed outstanding proficiency in various machine learning applications, such as classification and regression. Gradient boosting builds a set of regression models in an iterative process, in which at each iteration, a regressor model is trained to reduce a given loss on a given objective. This paper proposes an
Carlos Ruiz Pastor +2 more
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Gradient and Newton Boosting for Classification and Regression [PDF]
Expert Systems With Applications, 2021 Boosting algorithms are frequently used in applied data science and in research. To date, the distinction between boosting with either gradient descent or second-order Newton updates is often not made in both applied and methodological research, and it is thus implicitly assumed that the difference is irrelevant.
Fabio Sigrist
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2023
In this chapter, we explore gradient boosting, a powerful ensemble machine learning method, for both regression and classification tasks. With a focus on accessibility, we minimize abstract mathematical theories and instead emphasize two concrete numerical examples with small datasets related to predicting house sale prices and ease of selling ...
Zhiyuan Wang +3 more
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In this chapter, we explore gradient boosting, a powerful ensemble machine learning method, for both regression and classification tasks. With a focus on accessibility, we minimize abstract mathematical theories and instead emphasize two concrete numerical examples with small datasets related to predicting house sale prices and ease of selling ...
Zhiyuan Wang +3 more
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Computational Statistics & Data Analysis, 2002
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Gradient boosting factorization machines
Proceedings of the 8th ACM Conference on Recommender systems, 2014Recommendation techniques have been well developed in the past decades. Most of them build models only based on user item rating matrix. However, in real world, there is plenty of auxiliary information available in recommendation systems. We can utilize these information as additional features to improve recommendation performance.
Chen Cheng +4 more
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Gradient Boosted Trees with Extrapolation
2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019Gradient boosted decision tree algorithms only make it possible to interpolate data. Therefore, the prediction quality degrades if one of the features, such as time, lies outside the boundaries of training data set. It is hard to eliminate these types of features because they can affect other features indirectly.
Alexey Malistov, Arseniy Trushin
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Structured Regression Gradient Boosting
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016We propose a new way to train a structured output prediction model. More specifically, we train nonlinear data terms in a Gaussian Conditional Random Field (GCRF) by a generalized version of gradient boosting. The approach is evaluated on three challenging regression benchmarks: vessel detection, single image depth estimation and image inpainting ...
Ferran Diego, Fred A. Hamprecht
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Reweighted-Boosting: A Gradient-Based Boosting Optimization Framework
IEEE Transactions on Neural Networks and Learning SystemsBoosting is a well-established ensemble learning approach that aims to enhance overall performance by combining multiple weak learners with a linear combination structure. It operates on the principle of using new learners to compensate for the shortcomings of previous learners and is known for its ability to reduce computational resource requirements ...
Guanxiong He +5 more
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A Comparative Analysis of Gradient Boosting Algorithms
Artificial Intelligence Review, 2021Gonzalo Martínez-Muñoz
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