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Related Experiment Video

Updated: Jul 7, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Discrete vector quantization for arbitrary distance function estimation.

J Oommen¹, I K Altinel, N Aras

¹Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 8, 2008

Summary

This study introduces a novel method combining vector quantization (VQ) and automata learning to estimate unknown distances between nodes. This approach offers superior accuracy for complex problems like logistics and location analysis.

Related Experiment Videos

Last Updated: Jul 7, 2026

Quantifying Intermembrane Distances with Serial Image Dilations

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Area of Science:

Computational intelligence
Machine learning
Operations research

Background:

Adaptive learning research spans diverse fields, notably neural networks and learning automata.
Estimating arbitrary distance functions is crucial for logistics and location analysis but often faces challenges with unknown or uncomputable distance metrics.

Purpose of the Study:

To develop a method integrating vector quantization (VQ) and discretized automata learning for computing arbitrary distance functions.
To address the challenge of estimating internode distances when the explicit distance function is unknown and uncomputable.

Main Methods:

Incorporation of vector quantization (VQ) principles and discretized automata learning.
Adaptive polarization of nodes into subregions using VQ.
Learning subregion parameters via various methods, including meta-domain VQ strategies.
Developing a system for distance estimation without explicit function derivation.

Main Results:

Rigorous testing using actual road-travel distances for cities in Turkey.
Achieved conclusive results demonstrating high accuracy in distance estimation.
Outperformed existing single and hybrid strategies in current benchmarks.

Conclusions:

The proposed hybrid approach effectively computes arbitrary distance functions.
The method provides accurate distance estimations for real-world applications like logistics.
This strategy represents a significant advancement in distance estimation for complex, unknown functions.