Exploring adaptive boosting (AdaBoost) as a platform for the predictive modeling of tangible collection usage

Author:

Walker Kevin W.

Publisher

Elsevier BV

Subject

Library and Information Sciences,Education

Reference42 articles.

1. Adabag: An R package for classification with boosting and bagging;Alfaro;Journal of Statistical Software,2013

2. Thriving in the age of accelerations: A brief look at the societal effects of artificial intelligence and the opportunities for libraries;Arlitsch;Journal of Library Administration,2017

3. Predicting book use in university libraries by synchronous obsolescence;Baba;Procedia Computer Science,2016

4. Robotics vs machine learning vs artificial intelligence: Identifying the right tools for the right problems;Bellam;Credit & Financial Management Review,2018

5. Boosting;Berk,2008

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1. Performance Augmentation of Base Classifiers Using Adaptive Boosting Framework for Medical Datasets;Applied Computational Intelligence and Soft Computing;2023-12-22

2. Hybrid Tree-Based Wetland Vulnerability Modelling;Springer Natural Hazards;2022

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