Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Unsupervised Kernel Function Building Using Maximization of Information Potential Variability
verfasst von : A. M. Álvarez-Meza, D. Cárdenas-Peña, Germán Castellanos-Dominguez
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer International Publishing
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We propose a kernel function estimation strategy to support machine learning tasks by analyzing the input samples using Renyi’s Information Metrics. Specifically, we aim to identify a Reproducing Kernel Hilbert Space spanning the most widely the information force among data points by the maximization of the information potential variability of Parzen-based pdf estimation. So, a Gaussian kernel bandwidth updating rule is obtained as a function of the forces induced by a given dataset. Our proposal is tested on synthetic and real-world datasets related to clustering and classification tasks. Obtained results show that presented approach allows to compute RKHS’s favoring data groups separability, attaining suitable learning performances in comparison with state of the art algorithms.