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Sub-Optimum Signal Linear Detector Using Wavelets and Support Vector Machines
The problem of known signal detection in Additive White Gaussian Noise i...
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Pseudo vs. True Defect Classification in Printed Circuits Boards using Wavelet Features
In recent years, Printed Circuit Boards (PCB) have become the backbone o...
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Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines
Current approaches in pulse detection use domain transformations so as t...
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A Unified SVM Framework for Signal Estimation
This paper presents a unified framework to tackle estimation problems in...
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From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines
In this paper we explore a connection between deep networks and learning...
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A Comparison of Neural Network Training Methods for Text Classification
We study the impact of neural networks in text classification. Our focus...
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Speech Recognition: Increasing Efficiency of Support Vector Machines
With the advancement of communication and security technologies, it has ...
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Wavelet Time Shift Properties Integration with Support Vector Machines
This paper presents a short evaluation about the integration of information derived from wavelet non-linear-time-invariant (non-LTI) projection properties using Support Vector Machines (SVM). These properties may give additional information for a classifier trying to detect known patterns hidden by noise. In the experiments we present a simple electromagnetic pulsed signal recognition scheme, where some improvement is achieved with respect to previous work. SVMs are used as a tool for information integration, exploiting some unique properties not easily found in neural networks.
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