Convergence and application of online active sampling using orthogonal pillar vectors
Jong-Min Park
Dept. of Electr. & Comput. Eng., San Diego State Univ., CA, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Sept. 2004
Volume: 26,
Issue: 9
On page(s): 1197-1207
ISSN: 0162-8828
INSPEC Accession Number: 8113554
Digital Object Identifier: 10.1109/TPAMI.2004.61
Current Version Published: 2004-07-26
Abstract
The analysis of convergence and its application is shown for the Active Sampling-at-the-Boundary method applied to multidimensional space using orthogonal pillar vectors. Active learning method facilitates identifying an optimal decision boundary for pattern classification in machine learning. The result of this method is compared with the standard active learning method that uses random sampling on the decision boundary hyperplane. The comparison is done through simulation and application to the real-world data from the UCI benchmark data set. The boundary is modeled as a nonseparable linear decision hyperplane in multidimensional space with a stochastic oracle.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
