function epsilonInsensitiveLoss( weights, x, y, eta, lambda, epsilon ) {
	var p = weights.innerProduct( x ) - y;

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

	if ( p > epsilon ) {
		weights.add( x, -eta );
	} else if ( p < -epsilon ) {
		weights.add( x, +eta );
	}
}
Given a new observation `(x,y)`, updates the weights using the epsilon-insensitive loss.

## Notes

The penalty of the epsilon-insensitive loss is the absolute value of the dot product of the weights and `x` minus `y` whenever the absolute error exceeds epsilon, and zero otherwise.

@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
@param {PositiveNumber} epsilon - insensitivity parameter