Our approach departs from a Bayesian perspective, and aims to construct an alternative probabilistic view in such a way that its maximum-a-posteriori (MAP) ...
Our approach departs from a Bayesian perspective, and aims to construct an alternative probabilistic view in such a way that its maximum-a-posteriori (MAP) ...
Abstract—Current existing multi-hyperplane machine ap- proach deals with high-dimensional and complex datasets by approximating the input data region using ...
The principles of the Bayesian Classification and the Fuzzy Pattern Recognition are introduced in this paper. Classification on 5 kinds of corn leaf diseases ...
Aug 13, 2019 · A hyperplane is, by definition, the geometric object collecting together all points that are: Orthogonal to a specified vector.
Missing: Multi- Recognition.
In this paper, we describe a Bayesian classification method that informatively combines diverse sources of information and multiple feature spaces for ...
Missing: Hyperplane | Show results with:Hyperplane
Sep 14, 2024 · In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and ...
Jan 1, 2019 · Combining Bayesian nonparametrics and a forward model selection strategy, we construct parsimonious Bayesian deep networks (PBDNs) that ...
We report experiments using bench-mark datasets in which these two methods achieve a reduction in the number of support vectors and kernel calculations needed.
We show that the multi-class support vector machine (MSVM) proposed by Lee et al. (2004) can be viewed as a MAP estimation procedure under an appropriate ...