 In this case, the space requirements are no longer guaranteed to be constant since it requires storing all previous data points, but the solution may take less time to compute with the addition of a new data point, as compared to batch learning techniques.A common strategy to overcome the above issues is to learn using mini-batches, which process a small batch of formula_23 data points at a time, this can be considered as pseudo-online learning for formula_24 much smaller than the total number of training points.Mini-batch techniques are used with repeated passing over the training data to obtain optimized out-of-core versions of machine learning algorithms, for example, stochastic gradient descent.