Results 261 to 270 of about 106,398 (294)

Wavelet-based gradient boosting

Statistics and Computing, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eugene Dubossarsky, Jerome H. Friedman, John T. Ormerod, Matthew P. Wand   +3 more
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Gradient boosting factorization machines

Proceedings of the 8th ACM Conference on Recommender systems, 2014
Recommendation 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, Fen Xia, Tong Zhang 0001, Irwin King, Michael R. Lyu   +4 more
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Multi-Task Gradient Boosting

, 2023
Gradient Boosting Machines (GBMs) have revealed outstanding proficiency in various machine learning applications, such as classification and regression.
Seyedsaman Emami, Carlos Ruiz Pastor, Gonzalo Martínez-Muñoz   +2 more
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Gradient Boosting

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, Sayed Ameenuddin Irfan, Christopher Teoh, Priyanka Hriday Bhoyar   +3 more
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Stochastic gradient boosting

Computational Statistics & Data Analysis, 2002
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Gradient Boosted Trees with Extrapolation

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), 2019
Gradient 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), 2016
We 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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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.
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Reweighted-Boosting: A Gradient-Based Boosting Optimization Framework

IEEE Transactions on Neural Networks and Learning Systems
Boosting 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, Zheng Wang 0037, Liaoyuan Tang, Weizhong Yu, Feiping Nie 0001, Xuelong Li 0001   +5 more
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Boosting Algorithms as Gradient Descent.

2000
We provide an abstract characterization of boosting algorithms as gradient decsent on cost-functionals in an inner-product function space. We prove convergence of these functional-gradient-descent algorithms under quite weak conditions. Following previous theoretical results bounding the generalization performance of convex combinations of classifiers ...
Mason, Llew, Baxter, Jonathan, Bartlett, Peter, Frean, Marcus   +3 more
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