DocumentCode
3082809
Title
Optimal filters for attribute generation and machine learning
Author
Birdwell, J. Douglas ; Horn, Roger D.
Author_Institution
Dept. of Electr. & Comput. Eng., Tennessee Univ., Knoxville, TN, USA
fYear
1990
fDate
5-7 Dec 1990
Firstpage
1537
Abstract
Extensions to inductive inference methods of machine learning are proposed which allow inference from dynamic information contained in sampled data signals. An optimization problem over a set of finite impulse response filters is posed which, while not convex, can provide good quality attributes for classification of signal sources. Characteristics of the optimization problem, possible methods of its solution, and results using nonlinear programming are discussed
Keywords
digital filters; inference mechanisms; learning systems; nonlinear programming; FIR filters; attribute generation; dynamic information; finite impulse response filters; inductive inference methods; machine learning; nonlinear programming; optimization; Classification algorithms; Classification tree analysis; Data mining; Entropy; Finite impulse response filter; Machine learning; Machine learning algorithms; Optimization methods; Testing; Tree graphs;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
Conference_Location
Honolulu, HI
Type
conf
DOI
10.1109/CDC.1990.203869
Filename
203869
Link To Document