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Related Concept Videos

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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Classification of images using Gaussian copula model in empirical cumulative distribution function space.

Sapto Wahyu Indratno1, Sri Winarni2, Kurnia Novita Sari1

  • 1Statistics Research Group, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung West Java, Indonesia.

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Summary

This study introduces a novel image classification method using Gaussian copulas and Empirical Cumulative Distribution Function (ECDF) for better feature correlation understanding. The approach achieved high accuracy on the MNIST dataset, demonstrating its effectiveness.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional image classification often relies on direct pixel value representations.
  • Understanding the correlation structure between image features is crucial for improved classification.
  • Existing methods may not fully capture contextual relationships within images.

Purpose of the Study:

  • To introduce an innovative image classification approach using Gaussian copulas and Empirical Cumulative Distribution Function (ECDF).
  • To leverage distribution functions as feature descriptors for enhanced correlation analysis.
  • To develop a model that captures contextual relationships for more abstract image representations.

Main Methods:

  • Utilized Gaussian copulas combined with an Empirical Cumulative Distribution Function (ECDF) approach.
  • Employed Distribution Function of the Distribution Value (DFDV) as the margin distribution.
  • Applied Inference Function for Marginals (IFM) principles during the training phase.

Main Results:

  • The model achieved an average accuracy of 62.22% on the Modified National Institute of Standards and Technology (MNIST) dataset.
  • A peak accuracy of 96.92% was recorded, demonstrating significant performance.
  • The use of distribution functions effectively simplified feature description and improved correlation understanding.

Conclusions:

  • The proposed Gaussian copula and ECDF-based image classification model shows promising results.
  • This method offers a more abstract representation by understanding feature correlations.
  • The approach is effective for image classification tasks, as validated by MNIST dataset performance.