A Novel Supervised Learning Based on Density

Umut Orhan; Mahmut Hekim

Article Details

A Novel Supervised Learning Based on Density

Authors:

Umut Orhan | Mahmut Hekim

Publisher:

Tokat Gaziosmanpasa University

Journal:

Journal of New Theory 2015 Issue 1, pp.17-23

Document Type:

Article

Publication Date:

12/2014

Document Language:

Turkish

Document ID (Org No.):

2000491478

Frequency:

Quarterly

pISSN:

2149-1402

Abstract:

 

– Because prototype based classifiers are both easy and reasonable methods, there have been many studies on similarity based supervised learning. In order to detect each class region, they should not only appropriately locate the prototypes, but also deal with overfitting and instability. In this study, by considering all these criteria, we develop a new classifier method based on the prototypes selected from dense patterns. While the method determines details of the prototypes, it evades overfitting according to relation of the correct classification accuracy and the number of prototypes. Because of its similarity in point of architecture, we compare it with learning vector quantization (LVQ) method by using some synthetic and benchmark datasets. This comparison shows that our method is better than the other, and it may cause new suggestions on classification and some real applications.

Article Details

A Novel Supervised Learning Based on Density

Authors: Umut Orhan | Mahmut Hekim

Publisher: Tokat Gaziosmanpasa University

Journal: Journal of New Theory 2015 Issue 1, pp.17-23

Document Type: Article

Publication Date: 12/2014

Document Language: Turkish

Document ID (Org No.): 2000491478

Frequency: Quarterly

pISSN: 2149-1402

Issues

Authors:

Umut Orhan | Mahmut Hekim

Publisher:

Tokat Gaziosmanpasa University

Journal:

Journal of New Theory 2015 Issue 1, pp.17-23

Document Type:

Article

Publication Date:

12/2014

Document Language:

Turkish

Document ID (Org No.):

2000491478

Frequency:

Quarterly

pISSN:

2149-1402