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Related Experiment Videos

Extended input space support vector machine.

Ricardo Santiago-Mozos1, Fernando Perez-Cruz, Antonio Artes-Rodriguez

  • 1College of Engineering and Informatics, National University of Ireland Galway, Galway, Ireland. ricardo.santiago-mozos@nuigalway.ie

IEEE Transactions on Neural Networks
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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This study explores reducing classifier error by using multiple test samples. Two methods are presented: combining classifier outputs and building a new classifier, with the latter showing more accurate results for classification boundary estimation.

Area of Science:

  • Machine Learning
  • Statistical Pattern Recognition

Background:

  • Classifier error rates can be too high for practical applications.
  • Reducing error by gathering more test samples is a common strategy.
  • The Neyman-Pearson lemma offers solutions for known likelihoods, but not for unknown likelihoods with training data.

Purpose of the Study:

  • To investigate methods for reducing classifier error probability using additional test samples.
  • To explore alternatives to the Neyman-Pearson lemma when likelihoods are unknown.
  • To compare the effectiveness of combining classifier outputs versus building a new classifier.

Main Methods:

  • Combining soft (probabilistic) outputs from a classifier to achieve consensus labeling for K test samples.
  • Developing a new classifier that directly computes labels for K test samples, requiring an extended input space and incorporating known symmetries.

Related Experiment Videos

  • Illustrating the proposed methods using well-known databases.

    • Main Results:

    • The approach of building a new classifier yields more accurate results than combining outputs.
    • The new classifier approach primarily requires an accurate classification boundary.
    • Combining outputs necessitates an accurate posterior probability estimate across the entire input space.

    Conclusions:

    • A novel classifier designed for K test samples offers improved accuracy by focusing on the classification boundary.
    • This method provides a more robust solution for error reduction when likelihoods are unknown and training data is available.
    • The study demonstrates practical applications in machine learning by enhancing classifier performance.