Learning from Labeled and Unlabeled Data Using Random Walks-Reference-Cited by-同舟云学术

Learning from Labeled and Unlabeled Data Using Random Walks

Published:2004 Issue: Volume: Page:237-244
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Zhou Dengyong,Schölkopf Bernhard

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-540-28649-3_29.pdf

Reference11 articles.

1. Aldous, D., Fill, J.: Reversible Markov Chains and Random Walks on Graphs (in Preparation), http://stat-www.berkeley.edu/users/aldous/RWG/book.html

2. Belkin, M., Matveeva, I., Niyogi, P.: Regression and regularization on large graphs. Technical report, University of Chicago (2003)

3. Chan, T., Shen, J.: Variational restoration of non-flat image features: Models and algorithms. SIAM Journal of Applied Mathematics 61(4), 1338–1361 (2000)

4. Regional Conference Series in Mathematics;F. Chung,1997

5. Devijver, P.A., Kittier, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, London (1982)

Cited by 39 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. Efficient graph-based spectral techniques for data with few labeled samples;International Journal of Data Science and Analytics;2023-07-01

2. Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification;IEEE Access;2023

3. Improved Multi-label Propagation for Small Data with Multi-objective Optimization;Machine Learning and Knowledge Discovery in Databases: Research Track;2023

4. Anchor graph semi-supervised learning method based on Evidence Theory;2022 7th IEEE International Conference on Data Science in Cyberspace (DSC);2022-07

5. Network embedding based link prediction in dynamic networks;Future Generation Computer Systems;2022-02