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Such a sequence can be stochastic or deterministic.
The number of iterations is then decoupled to the number of points (each point can be considered more than once).
The learning paradigm of progressive learning, is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes.
Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the classifier gets remodeled automatically and the parameters are calculated in such a way that it retains the knowledge learnt thus far.
The ideas are general enough to be applied to other settings, for e.g. In the setting of supervised learning with the square loss function, the intent is to minimize the empirical loss, In practice, one can perform multiple stochastic gradient passes (also called cycles or epochs) over the data.
The choice of loss function here gives rise to several well-known learning algorithms such as regularized least squares and support vector machines.The incremental gradient method can be shown to provide a minimizer to the empirical risk.Kernels can be used to extend the above algorithms to non-parametric models (or models where the parameters form an infinite dimensional space).Mini-batch techniques are used with repeated passing over the training data to obtain optimized out-of-core versions of machine learning algorithms, for e.g. When combined with backpropagation, this is currently the de facto training method for training artificial neural networks.The simple example of linear least squares is used to explain a variety of ideas in online learning.
is a space of functions called a hypothesis space, so that some notion of total loss is minimised.