When to Consider Neural Networks

• Input is high-dimensional discrete or real-valued (e.g. raw sensor input)
• Output is discrete or real valued
• Output is a vector of values
• Possibly noisy data
• Form of target function is unknown
• Human readability of result is unimportant

Examples:

• Speech phoneme recognition [Waibel]
• Image classification [Kanade, Baluja, Rowley]
• Financial prediction