Inductive Learning

INDUCTIVE LEARNING

Inductive Learning in supervised learning we have a set of {xi, f (xi)} for 1≤i≤n, and our aim is to determine 'f' by some adaptive algorithm. It is a machine learning approach in which rules are inferred from facts or data. In logic, reasoning from the specific to the general Conditional or antecedent reasoning. Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given samples that are labeled in some useful way. In case of inductive learning algorithms, like artificial neural networks, the real robot may learn only from previously gathered data. Another option is to let the bot learn everything around him by inducing facts from the environment. This is known as inductive learning. Finally, you could get the bot to evolve, and optimise his performance over several generations.

f(x) is the target function

An example is a pair [x, f(x)]

Learning task: find a hypothesis h such that h(x)

F(x) gives a training set of examples D = {[xi,

f(xi) ]}, i = 1,2,…,N Construct h so that it agrees with f.

The hypothesis h is consistent if it agrees with f on all observations.

Ockham’s razor: Select the simplest consistent hypothesis.

How achieve good generalization?

Simplest: Construct a decision tree with one leaf for every example = memory based learning. Not very good generalization.

Advanced: Split on each variable so that the purity of each split increases (i.e. either only yes or only no)