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Taxonomy01:31

Taxonomy

Taxonomy is the science of defining and naming groups of biological organisms based on shared characteristics. It uses a hierarchy of increasingly inclusive categories with Latin names. The smallest units of taxonomy, species and genus, are used to assign a formal, taxonomic name to each species in a system. This classification system, referred to as binomial nomenclature, was formalized by Carolus Linnaeus in the 18th century.Hierarchy of TaxonomyThe hierarchy that Carolus Linnaeus first...
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Related Experiment Video

Updated: Jul 7, 2026

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

Appetitive Associative Olfactory Learning in Drosophila Larvae

Published on: February 18, 2013

String taxonomy using learning automata.

B J Oommen1, E V De St Croix

  • 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
|January 1, 1997
PubMed
Summary

This study introduces a novel learning-automaton approach for the String Taxonomy Problem, enabling efficient classification of noisy strings by partitioning dictionaries. The Object Migrating Automaton effectively clusters similar strings, demonstrated with macromolecular data.

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Last Updated: Jul 7, 2026

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

Appetitive Associative Olfactory Learning in Drosophila Larvae

Published on: February 18, 2013

Area of Science:

  • Computer Science
  • Bioinformatics
  • Machine Learning

Background:

  • Syntactic pattern recognition (PR) often involves comparing noisy strings against extensive dictionaries.
  • Hierarchical classification, by partitioning dictionaries into subdictionaries, can simplify PR tasks.
  • The "String Taxonomy Problem" of grouping similar strings into subsets lacks a definitive solution.

Purpose of the Study:

  • To present a novel solution for the String Taxonomy Problem using learning automata.
  • To demonstrate the efficacy of the Object Migrating Automaton for string taxonomy.

Main Methods:

  • A learning-automaton based approach is employed, specifically utilizing the Object Migrating Automaton.
  • The method partitions a dictionary of strings into subsets of similar strings.
  • The approach was tested using random strings and corrupted representations of macromolecular fragments.

Main Results:

  • The Object Migrating Automaton successfully clustered objects and images in prior work.
  • The proposed scheme effectively addressed the String Taxonomy Problem.
  • Demonstrated capability in handling noisy and garbled string representations of biological data.

Conclusions:

  • The presented learning-automaton based method offers a viable solution to the String Taxonomy Problem.
  • The Object Migrating Automaton is a powerful tool for string similarity clustering and classification.
  • This approach has potential applications in bioinformatics, particularly for analyzing macromolecular sequences.