Current Slide

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