Combining networks
	• When the amount of training data is limited, we need to avoid overfitting. – Averaging the predictions of many different networks is a good way to do this. – It works best if the networks are as different as possible. • If the data is really a mixture of several different “regimes” it is helpful to identify these regimes and use a separate, simple model for each regime. – We want to use the desired outputs to help cluster cases into regimes. Just clustering the inputs is not as efficient.