NDLI: Tri-Training Based Learning from Positive and Unlabeled Data

Content Provider	IEEE Xplore Digital Library
Author	Bangzuo Zhang Wanli Zuo
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun (Bangzuo Zhang; Wanli Zuo)
Abstract	This paper studies the problem of learning text classifier using positive and unlabeled examples with tri-training algorithm, which has been brought forward for semi-supervised learning. The key feature is that there are no negative examples. This paper proposed a new tri-training algorithm for the LPU problem that combines the step 1 of the three LPU algorithms to extract a reliable negative examples set, consequently to build an initial classifier for the tri-training and replace the bootstrap sampling procedure that has not been thought as a good method, and then iteratively use the three SVM classifiers until they convergence. Experiments on the popular Reuter21578 collection show the effectiveness of our proposed technique.
Starting Page	640
Ending Page	644
File Size	356174
Page Count	5
File Format	PDF
ISBN	9780769531519
DOI	10.1109/ISIP.2008.69
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-23
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tri-training Educational institutions Convergence Learning From Positive And Unlabeled Data Support vector machines Semi-supervised Learning Bayesian methods Supervised learning Support vector machine classification Training data Semisupervised learning Sampling methods Iterative algorithms
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Learning from Positive and Unlabeled Examples: A Survey

Co-EM Support Vector Machine Based Text Classification from Positive and Unlabeled Examples

A Novel Semi-Supervised SVM Based on Tri-Training

A hybrid selection method of helpful unlabeled data applicable for semi-supervised learning algorithms

Semi-supervised Learning from General Unlabeled Data

Unsupervised and Semi-Supervised Two-class Support Vector Machines

A new semi-supervised support vector machine learning algorithm based on active learning

The Model Selection for Semi-Supervised Support Vector Machines

A Survey of Semi-Supervised Learning Methods

Tri-Training Based Learning from Positive and Unlabeled Data

Similar Documents

Learning from Positive and Unlabeled Examples: A Survey

Co-EM Support Vector Machine Based Text Classification from Positive and Unlabeled Examples

A Novel Semi-Supervised SVM Based on Tri-Training

A hybrid selection method of helpful unlabeled data applicable for semi-supervised learning algorithms

Semi-supervised Learning from General Unlabeled Data

Unsupervised and Semi-Supervised Two-class Support Vector Machines

A new semi-supervised support vector machine learning algorithm based on active learning

The Model Selection for Semi-Supervised Support Vector Machines

A Survey of Semi-Supervised Learning Methods

Tri-Training Based Learning from Positive and Unlabeled Data