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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Vector Algebra: Method of Components01:08

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Related Experiment Video

Updated: Jun 18, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Online signature verification with support vector machines based on LCSS kernel functions.

Christian Gruber1, Thiemo Gruber, Sebastian Krinninger

  • 1Elektrobit Corporation, Munich, Germany.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

A novel technique uses the Longest Common Subsequences (LCSS) algorithm within Support Vector Machines (SVM) for reliable online signature verification. This SVM-LCSS method significantly outperforms existing techniques like SVM with Dynamic Time Warping (DTW).

Related Experiment Videos

Last Updated: Jun 18, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

Area of Science:

  • Biometrics
  • Machine Learning
  • Pattern Recognition

Background:

  • Online signature verification is crucial for security.
  • Existing methods may struggle with natural signal variations.
  • Support Vector Machines (SVM) are effective classification tools.

Purpose of the Study:

  • To introduce a new online signature verification technique using Longest Common Subsequences (LCSS) integrated into an SVM framework.
  • To evaluate the performance and reliability of the proposed SVM-LCSS method.
  • To compare SVM-LCSS against other kernel functions, specifically Dynamic Time Warping (DTW).

Main Methods:

  • Integration of the LCSS algorithm as a kernel function for SVM.
  • Utilizing LCSS to measure similarity in signature time series, accounting for local signal variability.
  • Testing the SVM-LCSS model on a proprietary database and the SVC 2004 benchmark dataset.

Main Results:

  • The SVM-LCSS technique demonstrates highly reliable person authentication.
  • Performance analysis shows SVM-LCSS significantly surpasses SVM with DTW kernel.
  • Parameterization of the SVM-LCSS was investigated, optimizing its effectiveness.

Conclusions:

  • The proposed SVM-LCSS method offers a superior approach to online signature verification.
  • LCSS effectively captures the nuances of signature dynamics, improving accuracy.
  • This technique provides a more reliable and performant solution for biometric security.