Summary
- Advantages
- Parallel processing
- Distributed representations
- Online (i.e., incremental) algorithm
- Simple computations
- Robust with respect to noisy data
- Robust with respect to node failure
- Empirically shown to work well for many problem domains
- Disadvantages
- Slow training
- Poor interpretability
- Network topology layouts are ad hoc
- Hard to debug because distributed representations
preclude content checking
- May converge to a local, not global, minimum of error
- Not known how to model higher-level cognitive mechanisms
- May be hard to describe a problem in terms of features
with numerical values