DocumentCode
2923433
Title
Online local linear classification
Author
Wang, Jiacheng ; Trapeznikov, Kirill ; Saligrama, Venkatesh
Author_Institution
Dept. of Electr. & Comput. Eng., Boston Univ., Boston, MA, USA
fYear
2013
fDate
15-18 Dec. 2013
Firstpage
173
Lastpage
176
Abstract
We present a novel convex formulation to learning binary, 2-region local linear classifiers. From this convex formulation, we formulate an online optimization scheme using stochastic gradient descent that allows for efficient training using streaming training data. We demonstrate the fast convergence and accurate classification on the canonical XOR dataset.
Keywords
convex programming; data handling; gradient methods; learning (artificial intelligence); pattern classification; training; canonical XOR dataset; convex formulation; learning binary classifier; online local linear classification; online optimization; stochastic gradient descent method; streaming training data; two-region local linear classifier; Fasteners; Support vector machine classification;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE 5th International Workshop on
Conference_Location
St. Martin
Print_ISBN
978-1-4673-3144-9
Type
conf
DOI
10.1109/CAMSAP.2013.6714035
Filename
6714035
Link To Document