Analysis of Perceptron-Based Active Learning-Reference-Cited by-同舟云学术

Analysis of Perceptron-Based Active Learning

Published:2005 Issue: Volume: Page:249-263
ISSN:0302-9743
Container-title:Learning Theory
language:
Short-container-title:

Author:

Dasgupta Sanjoy,Kalai Adam Tauman,Monteleoni Claire

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11503415_17.pdf

Reference12 articles.

1. Lecture Notes in Artificial Intelligence;D. Angluin,2001

2. Baum, E.B.: The perceptron algorithm is fast for nonmalicious distributions. Neural Computation 2, 248–260 (1997)

3. Blum, A., Frieze, A., Kannan, R., Vempala, S.: A polynomial-time algorithm for learning noisy linear threshold functions. In: Proc. 37th IEEE Symposium on the Foundations of Computer Science (1996)

4. Cesa-Bianchi, N., Gentile, C., Zaniboni, L.: Worst-case analysis of selective sampling for linear-threshold algorithms. In: Advances in Neural Information Processing Systems, vol. 17 (2004)

5. Dasgupta, S.: Analysis of a greedy active learning strategy. In: Advances in Neural Information Processing Systems, vol. 17 (2004)

Cited by 41 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. Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study;JMIR Medical Informatics;2024-07-02

2. Active learning for data streams: a survey;Machine Learning;2023-11-20

3. Few-shot Named Entity Recognition: definition, taxonomy and research directions;ACM Transactions on Intelligent Systems and Technology;2023-07-18

4. Value-Aware Active Learning;Frontiers in Artificial Intelligence and Applications;2023-06-22

5. Stream-Based Active Distillation for Scalable Model Deployment;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06