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Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

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Published on: October 28, 2018

Probabilistic self-organizing maps for continuous data.

Ezequiel Lopez-Rubio¹

¹Department of Computer Languages and Computer Science, University of Málaga, Málaga 29071, Spain. ezeqlr@lcc.uma.es

IEEE Transactions on Neural Networks

|August 24, 2010

Summary

This summary is machine-generated.

Probabilistic self-organizing maps enhance computational intelligence by integrating probability distributions. This review unifies theoretical frameworks, exploring distributions, self-organization, and learning schemes for classification and visualization.

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Area of Science:

Computational Intelligence
Machine Learning
Data Mining

Background:

Original self-organizing feature maps lacked input space probability distributions.
Probabilistic methodologies offer significant advantages for self-organizing map models.
This has spurred diverse proposals in probabilistic computational intelligence.

Purpose of the Study:

To provide a comprehensive overview of probabilistic self-organizing maps.
To unify theoretical frameworks, including estimation theories.
To examine probability distributions, self-organization mechanisms, and learning schemes.

Main Methods:

Reviewing state-of-the-art probabilistic approaches to self-organizing maps.
Examining underlying estimation theories: Expectation Maximization and Stochastic Approximation.
Analyzing continuous probability distributions, self-organization mechanisms, and learning schemes.

Main Results:

Identified two main theoretical frameworks: Expectation Maximization and Stochastic Approximation.
Detailed common continuous probability distributions, self-organization mechanisms, and learning schemes.
Highlighted connections and relative advantages of different approaches.

Conclusions:

Probabilistic self-organizing maps represent a significant advancement in computational intelligence.
Understanding the interplay between distributions, mechanisms, and learning is crucial for optimal performance.
Evaluated performance in classification and visualization tasks, demonstrating practical utility.