The proposed technique gives the flexibility required to model complex data structures that originate from a wide range of class-conditional distributions.

The proposed technique first proposes an implicit nonlinear transformation of the data into a feature space seeking to fit normal distributions having a common ...

The purpose of this dissertation is to improve the quantitative definitions of classes by replacing the classical normal model with more flexible and powerful ...

This paper proposes method that implemented weighting scheme as an embedded feature selection on hyperspectral dataset provided by AVIRIS imaging spectometer.

Bibliographic details on Toward an optimal supervised classifier for the analysis of hyperspectral data.

May 1, 2003 · TOWARD AN OPTIMAL SUPERVISED CLASSIFIER. 3.1. Introduction. It is well known that in supervised classification problems the probability of ...

PDF | Recent remote sensing literature has shown that support vector machine (SVM) methods generally outperform traditional statistical and neural.

Toward an Optimal Supervised Classifier for the Analysis of Hyperspectral Data ; NÚMERO. Volumen: 42 Número: 1 (2004) ; MATERIAS. INGENIERÍA Y CONSTRUCCIÓN CIVIL

A kernel-based supervised classifier for the analysis of hyperspectral data ... Toward an optimal supervised classifier for the analysis of hyperspectral data.

In this work, we investigate the reliability of four well-known supervised learning algorithms, namely, Support Vector Machines SVM, Random Forests RF, K- ...