Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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:
Classification of Systems-II01:31

Classification of Systems-II

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 Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Melanosis of the esophagus: A rare entity.

Revista de gastroenterologia de Mexico (English)·2025
Same author

Trends in the development of nanovaccines against swine diseases.

Vaccine·2025
Same author

A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe.

European archives of psychiatry and clinical neuroscience·2024
Same author

Pre-and post-surgical non-functional pituitary adenomas and their relationship with high levels of serum glucose.

The International journal of neuroscience·2024
Same author

Pre- and post-clinical-radiological and surgical evaluation of patients with pituitary adenoma and metabolic syndrome.

The International journal of neuroscience·2023
Same author

Immunostimulant Activity of Bacteria Isolated from Extreme Environments in Baja California Sur, Mexico: A Bioprospecting Approach.

Indian journal of microbiology·2022

Related Experiment Video

Updated: Jul 6, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

A Note on the bias in SVMs for multiclassification.

L Gonzalez-Abril1, C Angulo, F Velasco

  • 1Department of Applied Economics I, University of Seville, Seville, Spain. luisgon@us.es

IEEE Transactions on Neural Networks
|April 9, 2008
PubMed
Summary

For multiclass Support Vector Machines (SVM), different bias calculation methods can improve accuracy. Empirical tests show varied performance, with no single bias formulation consistently outperforming others for SVM classification.

Related Experiment Videos

Last Updated: Jul 6, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Standard Support Vector Machines (SVM) for biclassification determine bias post-hoc, typically midway between separating hyperplanes.
  • Extending SVM to multiclass problems requires specific strategies for bias calculation.

Purpose of the Study:

  • To explore and evaluate various bias calculation approaches for multiclass Support Vector Machines (SVM).
  • To determine if specific bias formulations can enhance classification accuracy in multiclass SVM.

Main Methods:

  • Review of different bias calculation strategies for multiclass SVM.
  • Empirical experimentation comparing the performance of these strategies.

Main Results:

  • The study confirms that employing distinct bias formulations can lead to improvements in classification accuracy.
  • No single bias calculation method demonstrated superior performance across all tested scenarios.

Conclusions:

  • Bias calculation is a critical factor in multiclass SVM performance.
  • Further research into optimized bias formulations for multiclass SVM is warranted.