technologies. We integrate patent big data for technological integration and use text mining, boosting, and ensemble for integrated … technologies and analyze the integrated patent data using regression trees, random forest, extreme gradient boosting, and ensemble …

precise convergence rates for satellite gradiometry, L2-boosting, and errors in variable problems. …

components and other shrinkage methods, including Bayesian model averaging and various bagging, boosting, LASSO and related …

In this paper various ensemble learning methods from machine learning and statistics are considered and applied to the … customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible … datasets using decision trees, ensemble versions of decision trees and the logistic regression model, which is a standard …

to determine attribute weights: a Poisson regression model and a novel boosting model minimizing Poisson deviance. We …

and boosting algorithms from the field of machine learning. Our system substantially outperforms the naive method of …

supervised learning. In addition, we investigate the effects of cost sensitive adaptations in CART and boosting and conclude that …

direct strategy using a boosting autoregression procedure at each horizon. First, we investigate the performance of the …

This paper applies component-wise boosting to the topic of regional economic forecasting. Component-wise boosting is a … combination with a large data set of monthly indicators, we show that boosting is generally doing a very good job in regional …, boosting outperforms our benchmark model for all the three regions considered. We also find that indicators that mirror the …