Soft Computing
  • Soft Computing
  • Introduction
  • Requirement
    • Data Set
  • Applications
    • Matlab
      • Perceptron
        • Little Red Riding Hood
          • Output
      • SVM
        • Code
        • Execution
      • TreeDecision
        • Code
        • Execution
      • Kmeans - Kmedoids
        • Code
        • Execution
      • Dimensionality Reduction
        • Principal component analysis (PAC)
          • Code
          • Execution
    • Python
      • Setup
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  • Soft Computing
  • Patter Recognition

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Introduction

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Last updated 5 years ago

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Soft Computing

Soft Computing is dedicated to system solutions based on soft computing techniques. It provides rapid dissemination of important results in soft computing technologies, a fusion of research in evolutionary algorithms and genetic programming, neural science and neural net systems, fuzzy set theory and fuzzy systems, and chaos theory and chaotic systems.

Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. By linking the ideas and techniques of soft computing with other disciplines, the journal serves as a unifying platform that fosters comparisons, extensions, and new applications. As a result, the journal is an international forum for all scientists and engineers engaged in research and development in this fast growing field.

Patter Recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).

The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods [dubious–discuss] and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use.

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