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INDUCTIVE CONFORMAL PREDICTOR FOR SPARSE CODING CLASSIFIERS: APPLICATIONS TO IMAGE CLASSIFICATION

Citation Author(s):
Kenneth E. Barner
Submitted by:
Sergio Matiz Romero
Last updated:
14 May 2019 - 10:41am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Samet Bayram
Paper Code:
ICASSP19005
 

Conformal prediction uses the degree of strangeness (nonconformity) of new data instances to determine the confidence values of new predictions. We propose an inductive conformal predictor for sparse coding classifiers, referred to as ICP-SCC. Our contribution is twofold: first, we present two nonconformitymeasures that produce reliable confidence values; second, we propose a batchmode active learning algorithm within the conformal prediction framework to improve classification performance by selecting training instances based on two criteria, informativeness and diversity. Experiments conducted on face and object recognition databases demonstrate that ICP-SCC improves the classification accuracy of state-of-the-art dictionary learning algorithms while producing reliable confidence values.

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