Classification Software System

A Classification Software System is a prediction system that applies a classification algorithm to solve a class prediction task.

• AKA: Mechanized Classifier, Automated Classification System, Machine Learning Classifier.
• Context:
  • It can perform Class Prediction through classification models.
  • It can enable Pattern Recognition through feature extraction.
  • It can support Decision Making through probability estimation.
  • It can handle Multi-class Classification through class discrimination.
  • It can maintain Model Performance through performance metrics.
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  • It can often implement Online Learning through incremental training.
  • It can often provide Real-time Classification through inference optimization.
  • It can often facilitate Batch Processing through parallel computation.
  • ...
  • It can range from being a Binary Classifier to being a Multi-class Classifier, depending on its output space.
  • It can range from being a Linear Classifier to being a Non-linear Classifier, depending on its model complexity.
  • It can range from being a Shallow Model to being a Deep Model, depending on its architectural depth.
  • ...
  • It can integrate with data preprocessing systems for feature preparation.
  • It can connect to model validation systems for performance evaluation.
  • It can support deployment systems for production serving.
  • ...
• Examples:
  • Traditional Classification Systems, such as:
    • Statistical Classifiers, such as:
      • Logistic Regression System for linear classification.
      • Naive Bayes System for probabilistic classification.
    • Tree-based Classifiers, such as:
      • Random Forest System for ensemble classification.
      • Decision Tree System for interpretable classification.
  • Neural Classification Systems, such as:
    • Feed-forward Neural Classifiers, such as:
      • Multi-layer Perceptron System for non-linear classification.
      • Deep Neural Network System for hierarchical classification.
    • Convolutional Neural Classifiers, such as:
      • CNN System for image classification.
      • Visual Recognition System for object detection.
  • ...
• Counter-Examples:
  • Regression Systems, which predict continuous values rather than discrete classes.
  • Clustering Systems, which perform unsupervised grouping rather than supervised classification.
  • Ranking Systems, which focus on order prediction rather than class assignment.
  • Point Estimation Systems, which estimate specific values rather than class membership.
• See: Classification Function, Machine Learning System, Pattern Recognition System, Statistical Learning System, Deep Learning System, Classification System, Computing System, Human Classifier, Classification Act.

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