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Neural Network as a Classifier

  • Weakness
    • Long training time
    • Require a number of parameters typically best determined empirically, e.g., the network topology or “structure.”
    • Poor interpretability: Difficult to interpret the symbolic meaning behind the learned weights and of “hidden units” in the network
  • Strength
    • High tolerance to noisy data
    • Ability to classify untrained patterns
    • Well-suited for continuous-valued inputs and outputs
    • Successful on an array of real-world data, e.g., hand-written letters
    • Algorithms are inherently parallel
    • Techniques have recently been developed for the extraction of rules from trained neural networks

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