Instance Based Learning
Ata Kaban
The University of Birmingham
1
,Today we learn:
K-Nearest Neighbours
Case-based reasoning
Lazy and eager learning
2
, Instance-based learning
One way of solving tasks of approximating
discrete or real valued target functions
Have training examples: (xn, f(xn)), n=1..N.
Key idea:
– just store the training examples
– when a test example is given then find the
closest matches
3
Ata Kaban
The University of Birmingham
1
,Today we learn:
K-Nearest Neighbours
Case-based reasoning
Lazy and eager learning
2
, Instance-based learning
One way of solving tasks of approximating
discrete or real valued target functions
Have training examples: (xn, f(xn)), n=1..N.
Key idea:
– just store the training examples
– when a test example is given then find the
closest matches
3
