On-line active learning: A new paradigm to improve practical useability of data stream modeling methods-Reference-Cited by-同舟云学术

On-line active learning: A new paradigm to improve practical useability of data stream modeling methods

Published:2017-11 Issue: Volume:415-416 Page:356-376
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Lughofer Edwin

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference50 articles.

1. Evolving Intelligent Systems — Methodology and Applications;Angelov,2010

2. Evolving fuzzy classifiers using different model architectures;Angelov;Fuzzy Sets Syst.,2008

3. Fast kernel classifiers with online and active learning;Bordes;J. Mach. Learn. Res.,2005

4. Contextual bandit for active learning: active Thompson sampling;Bouneffouf,2014

5. Active learning for regression based on query by committee;Burbidge,2007

Cited by 69 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Size stable batch mode model updating method;Vibrational Spectroscopy;2024-09

2. Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation;IEEE Internet of Things Magazine;2024-07

3. Real-Time Sampling Strategies for Regression with Irrelevant Features;International Journal of Semantic Computing;2024-05-29

4. Dynamic budget allocation for sparsely labeled drifting data streams;Information Sciences;2024-01

5. CADM$+$: Confusion-Based Learning Framework With Drift Detection and Adaptation for Real-Time Safety Assessment;IEEE Transactions on Neural Networks and Learning Systems;2024