INSTANCEBASE LEARNING
•
Instancebased learning methods simply store the training examples
instead of learning explicit description of the target function.
–
Generalizing the examples is postponed until a new instance must be classified.
–
When a new instance is encountered, its relationship to the stored examples is
examined in order to assign a target function value for the new instance.
•
Instancebased learning includes
nearest neighbor
,
locally weighted
regression
and
casebased reasoning
methods.
•
Instancebased methods are sometimes referred to as
lazy
learning
methods because they delay processing until a new instance must be
classified.
•
A key advantage of lazy learning is that instead of estimating the target
function once for the entire instance space, these methods can estimate
it locally and differently for each new instance to be classified.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentkNearest Neighbor Learning
•
kNearest Neighbor Learning
algorithm assumes all instances
correspond to points in the ndimensional space
R
n
•
The nearest neighbors of an instance are defined in terms of Euclidean
distance.
•
Euclidean distance between the instances
x
i
= <x
i1
,…,x
in
>
and
x
j
= <x
j1
,…,x
jn
> are:
•
For a given query instance x
q
,
f(x
q
) is calculated the function values of
knearest neighbor of x
q
∑
=

=
n
r
jr
ir
j
i
x
x
x
x
d
1
2
)
(
)
,
(
This is the end of the preview.
Sign up
to
access the rest of the document.
 Fall '08
 Demir
 Machine Learning, locally weighted regression, Xq, query instance xq

Click to edit the document details