The strong convergence of visual classification method and its applications
MetadataShow full item record
Visual classification method has been proposed as a learning strategy for pattern classification problem. In this paper, we show the strong convergence property of this method. In particular, the method is shown to converge to the Bayesian estimator, i.e., the learning error of the method is convergent to the posterior expected minimal value. The performance of the method has also been theoretically evaluated to comply with the human visual sensation and perception principle. The method is successfully used to some practical remote sensing and disease diagnosis applications. The experimental results all verify the validity and effectiveness of the theoretical conclusions. © 2013 Elsevier Inc. All rights reserved.
Showing items related by title, author, creator and subject.
Evaluation of discretization and integration methods for the analysis of finite hydrodynamic bearings with surface texturingWoloszynski, T.; Podsiadlo, P.; Stachowiak, Gwidon (2015)Efficient numerical methods are essential in the analysis of finite hydrodynamic bearings with surface texturing. This is especially evident in optimization and parametric studies where the discretization and integration ...
Adaptive antenna array beamforming using a concatenation of recursive least square and least mean square algorithmsSrar, Jalal Abdulsayed (2011)In recent years, adaptive or smart antennas have become a key component for various wireless applications, such as radar, sonar and cellular mobile communications including worldwide interoperability for microwave ...
Apergis, Nicholas; Christou, C.; Miller, S. (2012)This article analyzes the degree of convergence of financial development for a panel of 50 countries. We apply the methodology of Phillips and Sul (Econometrica 75:1771–1855, 2007) to various indicators of financial ...