JSM 2005 - Toronto

Abstract #303662

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 277
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #303662
Title: Semisupervised Machine Learning Technique
Author(s): Morteza Marzjarani*+
Companies: Saginaw Valley State University
Address: 7400 Bay Road, University Center, MI, 48710, United States
Keywords: Data Mining ; Statistics
Abstract:

Supervised and unsupervised machine learning techniques have been successful data-mining tools. Considering the advantages and disadvantages of these methods, it would be of interest to develop a model that satisfies the advantages of both methods. In this paper, I present the supervised and unsupervised machine learning techniques and develop a semisupervised method for machine learning. I will show that the method will take advantage of supervised learning and yet follow the unsupervised learning technique.

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Revised March 2005