Scholarlink

Self-Organizing Map (SOM), an unsupervised learning method, is an artificial neural network (ANN) able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more power ability is gained when the SOM-based model is merged with other clustering algorithms, creating hybrid frameworks and architectures. In this paper, hybrid frameworks and architectures are presented to improve the performance of a simple one-layer SOM in different domains such as image classification, assessing environmental pollutants, real-time scheduling, and other real-world problems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Short Analysis of Hybrid Frameworks Based on Self-organizing Maps to Improve Traditional Systems

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Dragan Simić,
  • José Luis Calvo-Rolle,
  • José R. Villar,
  • Svetislav D. Simić,
  • Svetlana Simić

摘要

Self-Organizing Map (SOM), an unsupervised learning method, is an artificial neural network (ANN) able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more power ability is gained when the SOM-based model is merged with other clustering algorithms, creating hybrid frameworks and architectures. In this paper, hybrid frameworks and architectures are presented to improve the performance of a simple one-layer SOM in different domains such as image classification, assessing environmental pollutants, real-time scheduling, and other real-world problems.