| Author | Vang Randy |
| Call Number | AIT Thesis no. CS-96-20 |
| Subject(s) | Neural networks (Computer science)
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering. |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. CS-96-20 |
| Abstract | Artificial Neural Networks are new technologies for classifications. They are able to process incomplete and imprecise data and to detect non-linear relations in the data. Artificial learning algorithms can be subdivided into two types, supervised and unsupervised. Neural networks learn in massively parallel and self-organizing way. Unsupervised learning neural networks, like Kohonen's self-organizing feature maps (Kohonen, 1989), learn the structure of high-dimensional data by mapping it on low-dimensional topologies, preserving the distribution and topology of the data. In this thesis the Kohonen self-organizing feature map is applied to classification of a land cover data set. The data was collected from existing database of land cover regions in Cambodia that were · expertly labeled into many classes. Rule extraction extracts land cover classes produced by self-organizing methods for the queries of knowledge. However, a rule generation algorithm of rule extraction out of the neural network, which could be used by the geological expert. |
| Year | 1996 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. CS-96-20 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Computer Science (CS) |
| Chairperson(s) | Ramakoti Sadananda |
| Examination Committee(s) | Yulu, Qi;Shrestha, Surendra |
| Scholarship Donor(s) | New Zealand. |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 1996 |
