About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Non-parametric feature selection for multiple class processes
Abstract
A method of feature selection is presented that has linear computational complexity, and ways are shown how to use it when the type of the probability density function is unknown. There is no claim that the procedures for nonparametric probability density function estimation are applicable to any thinkable distribution, but the lower bound for the classifier performance estimation makes the presented measure applicable in most practical cases. Simulations with synthetic test data as well as references to applications with real-world data demonstrate the applicability of the measure discussed.
Related
Conference paper
Actor conditioned attention maps for video action detection
Conference paper