Development of an Gaussian Process Model using a Data Filtering Method

[1]  Marcel J. T. Reinders,et al.  Classification in the presence of class noise using a probabilistic Kernel Fisher method , 2007, Pattern Recognit..

[2]  J Kocijan,et al.  Application of Gaussian processes for black-box modelling of biosystems. , 2007, ISA transactions.

[3]  Xingquan Zhu,et al.  Class Noise vs. Attribute Noise: A Quantitative Study , 2003, Artificial Intelligence Review.

[4]  Francisco Herrera,et al.  Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification , 2013, Pattern Recognit..

[5]  Amine Bermak,et al.  Gaussian process for nonstationary time series prediction , 2004, Comput. Stat. Data Anal..

[6]  Yukihiko Yamashita,et al.  Pattern recognition in the presence of noise , 1995, Pattern Recognit..

[7]  V. K. Jayaraman,et al.  An SVM classifier incorporating simultaneous noise reduction and feature selection: illustrative case examples , 2005, Pattern Recognit..

[8]  Carla E. Brodley,et al.  Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..

[9]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[10]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[11]  Karen Willcox,et al.  Surrogate Modeling for Uncertainty Assessment with Application to Aviation Environmental System Models , 2010 .

[12]  Alexander Y. Sun,et al.  Monthly streamflow forecasting using Gaussian Process Regression , 2014 .

[13]  Jiri Matas,et al.  Randomized RANSAC with Td, d test , 2004, Image Vis. Comput..