Machine Learning Paradigm

A Machine Learning Paradigm is a AI paradigm that defines fundamental approaches to machine learning problems based on the relationship between training data, learning objectives, and feedback mechanisms.

• AKA: ML Paradigm, Machine Learning Approach.
• Context:
  • It can typically determine Learning Process Structure through machine learning data requirements and machine learning feedback types.
  • It can typically influence Model Training Approach through machine learning objective functions and machine learning optimization methods.
  • It can typically shape Algorithm Selection through machine learning problem formulations and machine learning constraints.
  • It can typically guide Performance Evaluation Method through machine learning metrics and machine learning validation approaches.
  • It can typically establish Knowledge Representation Format through machine learning model structures and machine learning parameter types.
  • ...
  • It can often combine Multiple Learning Strategies through machine learning hybrid approaches.
  • It can often adapt to Domain-Specific Requirements through machine learning customizations.
  • It can often evolve with Technological Advancements through machine learning research innovations.
  • It can often integrate Human Knowledge through machine learning prior incorporations.
  • ...
  • It can range from being a Traditional Machine Learning Paradigm to being a Modern Machine Learning Paradigm, depending on its machine learning technological maturity.
  • It can range from being a Data-Intensive Machine Learning Paradigm to being a Knowledge-Intensive Machine Learning Paradigm, depending on its machine learning information source.
  • It can range from being a Single-Task Machine Learning Paradigm to being a Multi-Task Machine Learning Paradigm, depending on its machine learning scope.
  • ...
  • It can be implemented by Machine Learning Frameworks through machine learning algorithms.
  • It can be applied to Machine Learning Tasks through machine learning systems.
  • It can be evaluated using Machine Learning Benchmarks through machine learning performance measures.
  • ...
• Examples:
  • Supervised Machine Learning Paradigms, such as:
    • Classification Learning Paradigm for machine learning classification tasks.
    • Regression Learning Paradigm for machine learning regression tasks.
    • Structured Prediction Paradigm for machine learning structured output tasks.
  • Unsupervised Machine Learning Paradigms, such as:
    • Clustering Paradigm for machine learning grouping tasks.
    • Dimensionality Reduction Paradigm for machine learning feature reduction tasks.
    • Generative Modeling Paradigm for machine learning data generation tasks.
  • Semi-Supervised Machine Learning Paradigms, such as:
    • Self-Training Paradigm for machine learning pseudo-labeling tasks.
    • Co-Training Paradigm for machine learning multi-view tasks.
    • Graph-Based Semi-Supervised Paradigm for machine learning graph propagation tasks.
  • Reinforcement Learning Paradigms, such as:
    • Model-Based Reinforcement Learning Paradigm for machine learning environment modeling tasks.
    • Model-Free Reinforcement Learning Paradigm for machine learning direct policy learning tasks.
    • Inverse Reinforcement Learning Paradigm for machine learning reward inference tasks.
  • Transfer Learning Paradigms, such as:
    • Domain Adaptation Paradigm for machine learning domain shift tasks.
    • Few-Shot Learning Paradigm for machine learning limited data tasks.
    • Zero-Shot Learning Paradigm for machine learning unseen class tasks.
  • Meta-Learning Paradigms, such as:
    • Learning-to-Learn Paradigm for machine learning algorithm optimization tasks.
    • Model-Agnostic Meta-Learning Paradigm for machine learning rapid adaptation tasks.
    • Gradient-Based Meta-Learning Paradigm for machine learning parameter initialization tasks.
  • ...
• Counter-Examples:
  • Machine Learning Algorithm, which is a specific computational procedure rather than a machine learning paradigm.
  • Machine Learning Framework, which is an implementation tool rather than a machine learning paradigm.
  • Machine Learning Model, which is an instantiated system rather than a machine learning paradigm.
  • Machine Learning Technique, which is a specific method rather than a machine learning paradigm.
  • Machine Learning Architecture, which is a structural design rather than a machine learning paradigm.
• See: Learning Paradigm, Artificial Intelligence Paradigm, Statistical Learning Theory, Computational Learning Theory, Machine Learning Algorithm, Machine Learning System.

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