Image Home
Image People
Image Publication
Image Applications
Image Data & Code
Image Library
Image Seminar
Image Link
Image Album



Search LAMDA
»

CoForest

Description: CoForest is a semi-supervised algorithm, which exploits the power of ensemble learning and large amount of unlabeled data available to produce hypothesis with better performance.

Reference: M. Li and Z.-H. Zhou. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 2007, 37(6): 1088-1098.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (zhouzh@nju.edu.cn).

Requirement: To use this package, the whole WEKA environment (ver 3.4) must be available. Refer: I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA, 2000.

Data format: Both the input and output formats are the same as those used by WEKA.

ATTN2: This package was developed by Mr. Ming Li (lim@lamda.nju.edu.cn). This ReadMe file roughly explains the codes. For any problem concerning the code, please feel free to contact Mr. Li.



Download: code (6.4KB)
  Name Size
- coforest.rar 6.44 KB

Image
PoweredBy
(for FireFox 3+ and IE 7+)
Contact LAMDA: (email) contact@lamda.nju.edu.cn (tel) +86-25-89685926. © LAMDA, 2016