Alternate document:   Details   Learning in Gibbsian Fields: How Accurate and How Fast Can It Be? (02) Song Chun Zhu, Xiuwen Liu

See this document in CiteSeerX!

Learning from Labeled and Unlabeled Data with Label Propagation (2002)  (Make Corrections)  (4 citations)
Xiaojin Zhu, Zoubin Ghahramani
Carnegie Mellon University



  Home/Search   Context   Related

 
View or download:
gatsby.ucl.ac.uk/~...CALD02107.ps.gz
cmu.edu/~zhuxj/pub...CALD02107.ps.gz
Cached:  PS.gz  PS  PDF   Image  Update  Help

From:  gatsby.ucl.ac.uk/~zoubin...papers (more)
Homepages:  Z.Ghahramani  

Rate this article: (best)
  Comment on this article  
(Enter summary)

Abstract: We investigate the use of unlabeled data to help labeled data in classification. We propose a simple iterative algorithm, label propagation, to propagate labels through the dataset along high density areas defined by unlabeled data. We give the analysis of the algorithm, show its solution, and its connection to several other algorithms. We also show how to learn parameters by minimum spanning tree heuristic and entropy minimization, and the algorithm's ability to do feature selection.... (Update)

Similar documents based on text:   More   All
0.8:   Document Filtering Boosted By Unlabeled Data - Park, Zhang (2001)   (Correct)
0.6:   Learning Bayesian Network Classifiers with Labeled and.. - Cohen, Gozman, Cirelo   (Correct)
0.6:   Unlabeled Data Can Degrade Classification Performance of.. - Cozman, Cohen (2002)   (Correct)

Related documents from co-citation:   More   All
3:   Partially labeled classification with markov random walks - Szummer, Jaakkola - 2002
3:   Learning with labeled and unlabeled data - Seeger - 2000
2:   Cluster kernels for semi-supervised learning - Chapelle, Weston et al. - 2003

BibTeX entry:   (Update)

Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical report, Carnegie Mellon University, 2002. in preparation. http://citeseer.ist.psu.edu/zhu02learning.html   More

@techreport{ zhu02learning,
  author = "X. Zhu and Z. Ghahramani",
  title = "Learning from labeled and unlabeled data with label propagation",
  institution = "Carnegie Mellon University",
  number = "CMU-CALD-02-107",
  year = "2002",
  url = "citeseer.ist.psu.edu/zhu02learning.html" }
Citations (may not include all citations):
245   An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1999
109   the shortest spanning subtree of a graph and the traveling s.. (context) - Kruskal - 1956
54   A database for handwritten text recognition research (context) - Hull - 1994
47   Handwritten digit recognition with a back-propagation networ.. - Le Cun, Boser et al. - 1990
33   Learning with labeled and unlabeled data - Seeger - 2001
27   Learning from labeled and unlabeled data using graph mincut - Blum, Chawla - 2001
26   eigenvectors and stability (context) - Ng, Zheng et al. - 2001
10   Personal Communication (context) - Blum
8   usion kernels on graphs and other discrete input spaces (context) - Kondor, La et al. - 2002
7   A mean eld theory learning algorithm for neural networks (context) - Peterson, Anderson - 1987
6   Partially labeled classi cation with Markov random walks (context) - Szummer, Jaakkola - 2001
1   Towards semi-supervised classi cation with Markov random eld.. (context) - Zhu, Ghahramani - 2002

Documents on the same site (http://www.gatsby.ucl.ac.uk/~zoubin/papers.html):   More
An Introduction to Variational Methods for Graphical.. - Jordan, Ghahramani.. (1998)   (Correct)
Variational Learning for Switching State-Space Models - Ghahramani, Hinton (2000)   (Correct)
A Unifying Review of Linear Gaussian Models - Roweis, Ghahramani (1997)   (Correct)

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC