Wavelet Time Shift Properties Integration with Support Vector Machines

05/20/2005 ∙ by Jaime Gomez, et al. ∙ 0 ∙ share

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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Wavelet Time Shift Properties Integration with Support Vector Machines

Authors

Sub-Optimum Signal Linear Detector Using Wavelets and Support Vector Machines

Pseudo vs. True Defect Classification in Printed Circuits Boards using Wavelet Features

Upgrading Pulse Detection with Time Shift Properties Using Wavelets and Support Vector Machines

A Unified SVM Framework for Signal Estimation

From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines

A Comparison of Neural Network Training Methods for Text Classification

Speech Recognition: Increasing Efficiency of Support Vector Machines

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