| Author | Rao, G. R. M. Sudhakara |
| Call Number | AIT Thesis no.CS-94-16 |
| Subject(s) | Neural networks (Computer science)
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of
Master of Engineering, School of Engineering of Technology |
| Publisher | Asian Institute of Technology |
| Abstract | This thesis investigates Adaptive Resonance Theory 1 (ARTl) as a pattern clustering
algorithm. Inherent characteristics of the model are analyzed. In particular the vigilance
parameter, p, and its role in classification of patterns is examined. The experiments show that
the vigilance parameter as defined by Carpenter & Grossberg does not necessarily increase the
number of categories with its value, but decrease also, against the claim made by them. Hence,
the lemma, "Increasing p increases the total number of clusters learned and decreases the size
of each cluster" stated by Barbara Moore (1989), an MIT AI researcher, is not always valid.
A modified vigilance test criteria has been proposed, which takes into account, the problem of
subset & superset patterns and stably categorize, arbitrarily many input patterns in one list
presentation when the vigilance parameter is closer to one. The proposed method also performs
much better with regard to classification of patterns and reduces the number of list
presentations required for stable category learning. Better perfo1mance of new similarity
criteria with regard to noisy patterns also has been shown with experimental results. |
| Year | 1994 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Other Field of Studies (No Department) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Sadananda, Ramakoti
|
| Examination Committee(s) | Murai, Shunji ;Yulu, Qi
|
| Scholarship Donor(s) | ADB, Japan |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1994 |