DocumentCode
2768322
Title
On Geometric Structure of Quasi-Additive Learning Algorithms
Author
Ikeda, Kazushi
Author_Institution
Kyoto Univ., Kyoto
fYear
0
fDate
0-0 0
Firstpage
1123
Lastpage
1127
Abstract
Quasi-additive (QA) algorithms are a kind of online learning algorithms having two parameter vectors: one is an accumulation of input vectors and the other is a weight vector for prediction associated with the former by a non-linear function. We show that the vectors have a dually-flat structure from the information-geometric point of view, which makes it easier to discuss the convergence properties of the algorithms, as presented here.
Keywords
geometry; learning (artificial intelligence); nonlinear functions; convergence properties; information-geometric structure; nonlinear function; parameter vectors; quasiadditive learning algorithms; weight vector; Algorithm design and analysis; Convergence; Inference algorithms; Information analysis; Information geometry; Information theory; Mathematical programming; Neural networks; Nonlinear equations; Physics;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9490-9
Type
conf
DOI
10.1109/IJCNN.2006.246816
Filename
1716227
Link To Document