Forms of Learning

Several classification of learning systems are possible based on the above components as

follows:

Goal/Task/Target Function:

Prediction: To predict the desired output for a given input based on previous input/output

pairs. E.g., to predict the value of a stock given other inputs like market index, interest

rates etc.

Categorization: To classify an object into one of several categories based on features of

the object. E.g., a robotic vision system to categorize a machine part into one of the

categories, spanner, hammer etc based on the parts’ dimension and shape.

Clustering: To organize a group of objects into homogeneous segments. E.g., a satellite

image analysis system which groups land areas into forest, urban and water body, for

better utilization of natural resources.

Planning: To generate an optimal sequence of actions to solve a particular problem. E.g.,

an Unmanned Air Vehicle which plans its path to obtain a set of pictures and avoid

enemy anti-aircraft guns.

Models:

• Propositional and FOL rules

• Decision trees

• Linear separators

• Neural networks

• Graphical models

• Temporal models like hidden Markov models

Learning Rules:

Learning rules are often tied up with the model of learning used. Some common rules are

gradient descent, least square error, expectation maximization and margin maximization.

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Experiences:

Learning algorithms use experiences in the form of perceptions or perception action pairs

to improve their performance. The nature of experiences available varies with

applications. Some common situations are described below.

Supervised learning: In supervised learning a teacher or oracle is available which

provides the desired action corresponding to a perception. A set of perception action pair

provides what is called a training set. Examples include an automated vehicle where a set

of vision inputs and the corresponding steering actions are available to the learner.

Unsupervised learning: In unsupervised learning no teacher is available. The learner only

discovers persistent patterns in the data consisting of a collection of perceptions. This is

also called exploratory learning. Finding out malicious network attacks from a sequence

of anomalous data packets is an example of unsupervised learning.

Active learning: Here not only a teacher is available, the learner has the freedom to ask

the teacher for suitable perception-action example pairs which will help the learner to

improve its performance. Consider a news recommender system which tries to learn an

users preferences and categorize news articles as interesting or uninteresting to the user.

The system may present a particular article (of which it is not sure) to the user and ask

whether it is interesting or not.

Reinforcement learning: In reinforcement learning a teacher is available, but the teacher

instead of directly providing the desired action corresponding to a perception, return

reward and punishment to the learner for its action corresponding to a perception.

Examples include a robot in a unknown terrain where its get a punishment when its hits

an obstacle and reward when it moves smoothly.

In order to design a learning system the designer has to make the following choices

based on the application.