Active learning to recognize multiple types of plankton

Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The...

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Bibliographic Details
Main Authors: Tong Luo, Kramer, K., Samson, S., Remsen, A., Goldgof, D.B., Hall, L.O., Hopkins, T.
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Image recognition
Image sampling
Machine learning
Marine vegetation
Pattern recognition
Sampling methods
Support vector machine classification
Support vector machines
Text categorization
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Description
Summary:Active learning has been applied with support vector machines to reduce the data labeling effort in pattern recognition domains. However, most of those applications only deal with two class problems. In this paper, we extend the active learning approach to multiple class support vector machines. The experimental results from a plankton recognition system indicate that our approach often requires significantly less labeled images to maintain the same accuracy level as random sampling.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2004.1334570