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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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