In this paper, we will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the Symbolic Data Selection problem and it is ...

Dive into the research topics of 'Robust Gaussian kernel based approach for feature selection'. Together they form a unique fingerprint. Gaussian Kernel ...

This paper will incorporate similarity margin concept and Gaussian kernel fuzzy rough sets to deal with the Symbolic Data Selection problem and it is also ...

Robust Gaussian Kernel Based Approach for Feature Selection · List of references · Publications that cite this publication.

Fingerprint. Dive into the research topics of 'Robust Gaussian kernel based approach for feature selection'. Together they form a unique fingerprint.

The goal of this paper is to study the theoretical and empirical robustness of kernel-based algorithms within the framework of robust statistical estimation and ...

We present a scalable, robust system to find the best wavelet parameters using Gaussian processes (GPs). We demonstrate our system by assessing wavelets as ...

Gaussian processes (GPs) are used to make medical and scientific decisions, including in cardiac care and monitoring of atmospheric carbon dioxide levels.

This paper presents a new approach to a robust Gaussian process regression, creating a non-parametric Bayesian regression estimate robust to outliers.

Clustering is an important unsupervised learning problem in machine learning and statistics. Among many existing algorithms, kernel k-means has drawn much.