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Radial basis function neural network based on order statistics

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TLDR
The proposed neural network uses the Median M-type (MM) estimator in the scheme of radial basis function to train the neural network and was proved an accurate estimation of the implied probabilities.
Abstract
In this paper we present a new type of Radial Basis Function (RBF) Neural Network based in order statistics for image classification applications. The proposed neural network uses the Median M-type (MM) estimator in the scheme of radial basis function to train the neural network. The proposed network is less biased by the presence of outliers in the training set and was proved an accurate estimation of the implied probabilities. From simulation results we show that the proposed neural network has better classification capabilities in comparison with other RBF based algorithms.

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Citations
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Rank M-type radial basis function (RMRBF) neural network for Pap smear microscopic image classification

TL;DR: From simulation results, it is observed that the RMRBF neural network has better classification capabilities in comparison with other RBF based algorithms.
Journal ArticleDOI

A robust neuro-fuzzy classifier for the detection of cardiomegaly in digital chest radiographies

TL;DR: A novel procedure that automatically and reliably determines the presence of cardiomegaly in chest image radiographies is presented and the classification results of the proposed fuzzy method using a Radial Basis Function (RBF) neural network are improved in terms of accuracy, sensitivity, and specificity.
Book ChapterDOI

Diagnosis of Cervical Cancer Using the Median M-Type Radial Basis Function (MMRBF) Neural Network

TL;DR: From simulation results, it is observed that the MMRBF neural network has better classification capabilities in comparison with the Median RBF algorithm used as comparative.
Journal ArticleDOI

Artificial intelligent systems application in cervical cancer pathological cell image classification systems — a review

TL;DR: In this paper, a review of algorithms used for cervical cancer cell image classification is presented, this includes pre-processing steps (noise reduction and cell segmentation/without segmentation), feature extraction, and intelligent diagnosis systems and their evaluations.
Dissertation

Deteccion de insuficiencias cardiacas mediante la red neuronal de base radial difusa

TL;DR: A novel methodology capable of make a reliable diagnosis of cardiac insufficiencies, that would be useful as a support for the physician non-expert in cardiology, that was developed in MatlLab R2008 in a conventional compute system.
References
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Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Book

Neural Networks: A Systematic Introduction

Raúl Rojas
TL;DR: The authors may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now.
Book

Radial Basis Functions: Theory and Implementations

TL;DR: In this paper, a radial basis function approximation on infinite grids is proposed, based on the wavelet method with radial basis functions (WBFF) with compact support, which is a general method for approximation and interpolation.
Journal ArticleDOI

Radial Basis Functions

TL;DR: This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contributes useful new classes of radial basis function.
Journal ArticleDOI

Image processing with neural networks–a review

TL;DR: The various applications of neural networks in image processing are categorised into a novel two-dimensional taxonomy for image processing algorithms and their specific conditions are discussed in detail.
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