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)
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