Abstract
In this paper we present two supervised pattern classifiers designed using Boolean Neural Networks (BNN). They are (a) Nearest-to-an-Exemplar (NTE) and (b) Boolean K-Nearest Neighbor (BKNN) classifier. The classifiers use the idea of Radius of Attraction (ROA) to achieve their goal. Patterns are classified by constructing hyper spheres in feature space with the exemplar nodes as the centers for the NTE classifier and with nodes representing each of the training patterns as centers in the BKNN. The radii of these hyper spheres are to be adapted to the problem at hand. Both these classifiers are tested with well-known data sets of binary as well as continuous feature values. Results obtained are comparable with those obtained by similar existing classifiers.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Engineering Systems Through Artificial Neural Networks |
| Editors | C.H. Dagli, L.I. Burke, Y.C. Shin |
| Place of Publication | Fairfield, NJ, United States |
| Publisher | ASME |
| Pages | 5-13 |
| Number of pages | 9 |
| Volume | 2 |
| State | Published - Dec 1 1992 |
| Event | Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA Duration: Nov 15 1992 → Nov 18 1992 |
Other
| Other | Proceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 |
|---|---|
| City | St.Louis, MO, USA |
| Period | 11/15/92 → 11/18/92 |
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ASJC Scopus subject areas
- Software
Cite this
Design of supervised classifiers using Boolean Neural Networks. / Gazula, Srinivas; Kabuka, Mansur R.
Intelligent Engineering Systems Through Artificial Neural Networks. ed. / C.H. Dagli; L.I. Burke; Y.C. Shin. Vol. 2 Fairfield, NJ, United States : ASME, 1992. p. 5-13.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Design of supervised classifiers using Boolean Neural Networks
AU - Gazula, Srinivas
AU - Kabuka, Mansur R.
PY - 1992/12/1
Y1 - 1992/12/1
N2 - In this paper we present two supervised pattern classifiers designed using Boolean Neural Networks (BNN). They are (a) Nearest-to-an-Exemplar (NTE) and (b) Boolean K-Nearest Neighbor (BKNN) classifier. The classifiers use the idea of Radius of Attraction (ROA) to achieve their goal. Patterns are classified by constructing hyper spheres in feature space with the exemplar nodes as the centers for the NTE classifier and with nodes representing each of the training patterns as centers in the BKNN. The radii of these hyper spheres are to be adapted to the problem at hand. Both these classifiers are tested with well-known data sets of binary as well as continuous feature values. Results obtained are comparable with those obtained by similar existing classifiers.
AB - In this paper we present two supervised pattern classifiers designed using Boolean Neural Networks (BNN). They are (a) Nearest-to-an-Exemplar (NTE) and (b) Boolean K-Nearest Neighbor (BKNN) classifier. The classifiers use the idea of Radius of Attraction (ROA) to achieve their goal. Patterns are classified by constructing hyper spheres in feature space with the exemplar nodes as the centers for the NTE classifier and with nodes representing each of the training patterns as centers in the BKNN. The radii of these hyper spheres are to be adapted to the problem at hand. Both these classifiers are tested with well-known data sets of binary as well as continuous feature values. Results obtained are comparable with those obtained by similar existing classifiers.
UR - http://www.scopus.com/inward/record.url?scp=0027105373&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027105373&partnerID=8YFLogxK
M3 - Conference contribution
VL - 2
SP - 5
EP - 13
BT - Intelligent Engineering Systems Through Artificial Neural Networks
A2 - Dagli, C.H.
A2 - Burke, L.I.
A2 - Shin, Y.C.
PB - ASME
CY - Fairfield, NJ, United States
ER -