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Abstract: Machine learning applications require of reliable information to attain accurate results. However, real world data are never as good as we would like them to be and often can su#er from corruption, degrading the performance of processes on the data. Proposed methods for data cleaning eliminate not only erroneous data, but also rare-correct observations, resulting in unreliable data sets. (Update)
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BibTeX entry: (Update)
@misc{ department-kernel,
author = "Computer Science Department",
title = "Kernel Based Noise-Aware Machine",
url = "citeseer.ist.psu.edu/758225.html" }
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Documents on the same site (http://ccc.inaoep.mx/~hugojair/papers.html):
A Comparison of Outlier Detection Algorithms for - Machine Learning Jair (2005) (Correct)
Kernel Methods for Anomaly - Detection And Noise (Correct)
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