Ensemble methods combine a set of classifiers to construct a new classifier that is (often) more accurate than any of its component classifiers. In this paper, we use ensemble methods to identify noisy training examples. More precisely, we consider the problem of mislabeled training examples in classification tasks, and address this problem by pre-processing the training set, i.e. by identifyin...

Compression measures used in inductive learners, such as measures based on the MDL (Minimum Description Length) principle, provide a theoretically justiied basis for grading candidate hypotheses. Compression-based induction is appropriate also for handling of noisy data. This paper shows that a simple compression measure can be used to detect noisy examples. A technique is proposed in which noi...

he voice recognition is the method that calculates an optimal match between two given sequences with certain restrictions is called Dynamic time wrapping. The sequences are "warped" non-linearly in the time dimension to determine a measure of their similarity independent of certain non-linear variations in the time dimension. This sequence alignment method is often used in time series classific...

The Occam's razor principle suggests that among all the correct hypotheses, the simplest hypothesis is the one which best captures the structure of the problem domain and has the highest prediction accuracy when classifying new instances. This principle is implicitly used also for dealing with noise, in order to avoid overrtting a noisy training set by rule truncation or by pruning of decision ...

A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural networ...

Modelling and reconstruction methods are presented for noise reduction of autocorrelated signals in non-Gaussian, impulsive noise environments. A Bayesian probabilistic framework is adopted and Markov chain Monte Carlo methods are developed for detection and correction of impulses. Individual noise sources are modelled as Gaussian with unknown scale (variance), allowing for robustness tòheavy-t...

In this paper, the reduction of interferometric noise by superposition of high frequency modulation is analyzed. It is shown that the nature of this reduction is due to a redistribution of noise energy from baseband to higher frequencies where it can be discarded by low-pass filtering. Detailed analysis revealed the dependence of the noise reduction factor on the product fo T, and the modulatio...

Competitive neural networks can be used to e ciently quantize image and video data. We discuss a novel class of vector quantizers which perform noise robust data compression. The vector quantizers are trained to simultaneously compensate channel noise and code vector elimination noise. The training algorithm to estimate code vectors is derived by the maximum entropy principle in the spirit of d...

In the paper characteristic features of the corona noise from UHV transmission lines have been distinguished, which can be useful for the noise measurement under high level interference conditions. The utility of statistical methods in elimination of typical environmental interference has been shown, particularly methods based on the measurement of statistical spectra. The effect of inaccurate ...