Results 241 to 250 of about 197,316 (278)

Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis - an integrated bioinformatic approach. [PDF]

Front Oral Health
Yadalam PK, Sharma S, Natarajan PM, Ardila CM.   +3 more
europepmc   +1 more source

Forecasting Emergency Room Patient Volumes Using Extreme Gradient Boosting With Temporal and Seasonal Feature Engineering: A Comparative Study Across Hospitals. [PDF]

Cureus
Huang KA, Hardin WM, Prakash NS.
europepmc   +1 more source

Optimized Gradient Boosting Models for Adaptive Prediction of Uniaxial Compressive Strength in Carbonate Rocks Using Drilling Data. [PDF]

ACS Omega
Adjei S, Gowida A, Elkatatny S, Oleka JO.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

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
openaire   +1 more source

Stochastic gradient boosting

Computational Statistics & Data Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

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, Irwin King, Michael R. Lyu   +4 more
openaire   +1 more source

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, Liaoyuan Tang, Weizhong Yu, Feiping Nie, Xuelong Li   +5 more
openaire   +2 more sources

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
openaire   +1 more source

Wavelet-based gradient boosting

Statistics and Computing, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dubossarsky, E., Friedman, J. H., Ormerod, J. T., Wand, M. P.   +3 more
openaire   +1 more source

Gradient Boosting Machine

2018
So far, we’ve considered decision trees and random forest algorithms. We saw that random forest is a bagging (bootstrap aggregating) algorithm—it combines the output of multiple decision trees to give the prediction. Typically, in a bagging algorithm trees are grown in parallel to get the average prediction across all trees, where each tree is built on
openaire   +1 more source