function squaredErrorLoss( weights, x, y, eta, lambda ) {
	var loss = y - weights.innerProduct( x );

	// Perform L2 regularization...
	regularize( weights, lambda, eta );

	weights.add( x, ( eta * loss ) );
}
Given a new observation `(x,y)`, updates the weights using the squared error loss.

## Notes

The squared error loss is defined as the squared difference of the observed and fitted value.

@private
@param {WeightVector} weights - current model coefficients
@param {NumericArray} x - feature vector
@param {number} y - response value
@param {PositiveNumber} eta - current learning rate
@param {NonNegativeNumber} lambda - regularization parameter