Self-Organizing Map Demystified: Unravel the Myths and Power of SOM in Machine Learning
Self-Organizing Map Demystified: Unravel the Myths and Power of SOM in Machine Learning by Peter Leow
English | Dec 20, 2015 | ASIN: B019NPHMSU | 52 Pages | AZW3/MOBI/EPUB/PDF (conv) | 5.91 MB
English | Dec 20, 2015 | ASIN: B019NPHMSU | 52 Pages | AZW3/MOBI/EPUB/PDF (conv) | 5.91 MB
Self-Organizing Map (SOM) was introduced as an unsupervised competitive learning algorithm of the artificial neural networks (ANN) by Finnish Professor Teuvo Kohonen in the early 1980s. In Artificial Intelligence, SOM algorithm is considered one of the most powerful algorithms in the fields of data visualization and exploration and is widely used in clustering analysis in data mining. Its ability to map high-dimensional input vectors onto a two-dimensional grid of prototype vectors and orders them topologically greatly facilitates the interpretation by naked human eyes. This book takes you through the concept, architecture, and algorithm of SOM in theory, followed by an exercise to design and program a SOM to recognize handwritten digits using MATLAB. Full source code for the exercise is available for download.