Kohonen Maps

E. Oja · Samuel Kaski

jul. de 1999 · Elsevier

Sobre este e-book

The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.

The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.

Sobre o autor

Samuel Kaski received the DSc (PhD) degree in Computer Science from Helsinki University of Technology, Finland, in 1997. He is currently a Professor at Aalto University, the Director of Helsinki Institute for Information Technology HIIT, Aalto University and University of Helsinki, Finland, and the Director of Finnish Centre of Excellence in Computational Inference Research COIN. He is an action editor of the Journal of Machine Learning Research, and has chaired several conferences including AISTATS 2014. He has published over 200 peer-reviewed papers and supervised 18 PhD theses. His current research interests include statistical machine learning, computational biology and medicine, information visualization, and exploratory information retrieval.