A $-Family Friendly Approach To Prototype Selection

Keywords

$-family; Prototype selection; Classifier; Gesture recognition; Rapid prototyping; User interfaces

Abstract

We explore the benefits of intelligent prototype selection for $-family recognizers. Currently, the state of the art is to randomly select a subset of prototypes from a dataset without any processing. This results in reduced computation time for the recognizer, but also increases error rates. We propose applying optimization algorithms, specifically random mutation hill climb and a genetic algorithm, to search for reduced sets of prototypes that minimize recognition error. After an evaluation, we found that error rates could be reduced compared to random selection and rapidly approached the baseline accuracies for a number of different $-family recognizers.

Publication Date

3-7-2016

Publication Title

International Conference on Intelligent User Interfaces, Proceedings IUI

Volume

07-10-March-2016

Number of Pages

370-374

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2856767.2856808

Socpus ID

84963720342 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84963720342

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