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Machine Learning Processes

The property that is of primary significance for a neural network is the ability of the network to learn from its environment, and to improve its performance through learning.

The improvement in performance takes place over time in accordance with some prescribed measure.

A neural network learns about its environment through an interactive process of adjustments applied to its synaptic weights and bias levels.

Ideally, the network becomes more knowledgeable about its environment after each iterationof the learning process.

There are too many activities associated with the notion "learning" to justify defining it in a precise manner.

Moreover, the process of learningis a matter of view-point, which makes it all the more difficult to agree on a precise definition of the term.

For example, learning as viewed by a psychologist is quit different from learning in a classroom sense. Recognizing that our interest in machine learning neural networks, we use a definition of learning as follows:-

Learning is a process by which the free parameters of a neural network are adapted through a process of stimulation by the environment in which the neural is embedded. The type of learning is determined by the manner in which the parameter changes take place.

This definition of the learning process implies the following sequence of events:

  • The neural network is stimulated by an environment.
  • The neural network undergoes changes in its free paramenters as a result of this stimulation.
  • The neura; network responds in a new way to the environment because of the changes that have occurred in its internal structure.
A prescribed set of well-defined rules for the solution of a learning problem is called a learning algorithms.

As one would expect, there is no unique learning algorithm for the design of neural networks. Rather,we have a "kit of tools" represented by a diverse variety of learning algorithms, each of which offers advantages of its own.

Basically, learning algorithms differ from each other in the way in which the adjustment to a synaptic weight of a neuron is formulated.

Another factor to be considered is the manner in which a neural network (learning machine), made upof a set of inter-connected neurons, related to its environment.

In this latter context we speak of a learning paradigm thatrefers to a model of the environment in which the neural network operates.

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