# Alternatives to backpropagation

## Alternatives to backpropagation

### Cascade-correlation learning architecture

This is a method for both building and training.

We start with a bare bones network. We then add nodes one by one, training and then fixing their values.

### Extreme learning machines

This is an alternative to backprobagation for training a feedforward neural network.

We start with random parameters for each layer $$W_i$$.

We have:

$$\hat y=W_2\sigma (W_1 x)$$

Etc.

We calculate:

$$W_2=\sigma(W_1x)^+Y$$

So $$W_1$$ is random and not updated.

$$W_2$$ is assigned to minimise loss, where $$W_2$$ has no activation function.