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

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...
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,
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...

You might also read

Related Articles

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

Sort by
Same author

Performance and secondary chemistry of two hybrid aspen (Populus tremula L. x Populus tremuloides Michx.) clones in long-term elevated ozone exposure.

Journal of chemical ecology·2009
Same author

A constructive method for multivariate function approximation by multilayer perceptrons.

IEEE transactions on neural networks·1992
Same author

[Value of the flow-volume curve in the evaluation of non-obstructive dyspnea complaints].

Pneumologie (Stuttgart, Germany)·1990
Same author

Plasmodium berghei: lymphocyte and macrophage dynamics in the spleen of Balb/c mice in the course of infection and after rechallenge of cured mice.

Experimental parasitology·1988

Related Experiment Videos

Adaptive nearest neighbor pattern classification.

S Geva1, J Sitte

  • 1Queensland Univ. of Technol., Brisbane, Qld.

IEEE Transactions on Neural Networks
|January 1, 1991
PubMed
Summary

The Decision Surface Mapping (DSM) method, a variant of Learning Vector Quantization (LVQ), offers superior performance in pattern classification. DSM excels in accuracy, learning speed, and prototype efficiency for clearly defined class boundaries.

Area of Science:

  • Computer Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Nearest-neighbor (NN) and Learning Vector Quantization (LVQ) are established supervised learning algorithms.
  • Existing methods may face challenges with efficiency and prototype selection for complex decision boundaries.

Purpose of the Study:

  • To introduce and evaluate the Decision Surface Mapping (DSM) method, a novel variant within the LVQ family.
  • To compare DSM's performance against established algorithms like NN, LVQ, and error backpropagation.
  • To assess DSM's effectiveness in scenarios with sharply defined class boundaries.

Main Methods:

  • DSM utilizes a small subset of correctly classified training samples as prototypes.
  • Prototypes are adapted using the training set to map decision surfaces between classes.

Related Experiment Videos

  • Performance is benchmarked against NN, LVQ, and two-layer perceptrons trained via error backpropagation.

    • Main Results:

    • DSM demonstrates superior performance in terms of error rates and learning speed.
    • The algorithm requires fewer prototypes to effectively describe class boundaries compared to other methods.
    • These advantages are particularly pronounced when class boundaries are sharply defined, with no training set classification errors.

    Conclusions:

    • DSM is a fast and efficient supervised learning algorithm suitable for pattern classification.
    • The method offers significant improvements over traditional NN, LVQ, and backpropagation techniques.
    • DSM is especially effective for datasets with clear class separation, optimizing prototype usage and classification accuracy.