Multidimensional data classification with artificial neural networks
| dc.creator | Boinee, P. | |
| dc.creator | Barbarino, F. | |
| dc.creator | De Angelis, A. | |
| dc.date | 2004-12-06 | |
| dc.date.accessioned | 2026-07-07T03:22:09Z | |
| dc.date.available | 2026-07-07T03:22:09Z | |
| dc.description | Multi-dimensional data classification is an important and challenging problem in many astro-particle experiments. Neural networks have proved to be versatile and robust in multi-dimensional data classification. In this article we shall study the classification of gamma from the hadrons for the MAGIC Experiment. Two neural networks have been used for the classification task. One is Multi-Layer Perceptron based on supervised learning and other is Self-Organising Map (SOM), which is based on unsupervised learning technique. The results have been shown and the possible ways of combining these networks have been proposed to yield better and faster classification results. | |
| dc.description | 8 pages, 4 figures, Submitted to EURASIP Journal on Applied Signal Processing, 2004 | |
| dc.identifier | https://arxiv.org/abs/cs/0412023 | |
| dc.identifier | http://arxiv.org/abs/cs/0412023 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32481 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Artificial Intelligence | |
| dc.subject | F.1.1; K.3.2; I.2.6 | |
| dc.title | Multidimensional data classification with artificial neural networks | |
| dc.type | text |