Pattern Recognition
Major Steps:
- Feature extraction
In general, feature extraction is a difficult problem.
For instance, how do you find the distance between two eyes in
a person's portrait (as an digital image)?
If feature extraction can be done correctly,
then the following process of pattern recognition can be easy.
- Data preprocessing
- Data normalization
- Gaussian Normalization
- Unit Normalization
- Data reduction
- Editing: To remove noisy (inconsistent) data
Randomly select a point and find its nearest
neighbor. If these two points belong to
different classes, then they are inconsistent
data pairs and one of them has to be removed.
- Condensing: To remove redundant
(deeply embedded) data
Randomly select a point and find its nearest
neighbor. If these two points belong to
the same class, then they are redundant
data points and one of them has to be removed.
- Data classification
- K nearest neighbor rule (KNNR)
- When K is 1, the decision boundary can be obtained
via the Voronoi diagram.
- A large K leads to a more robust classifier.
- K is usually odd (3 or 5, etc) for
two-class problems to avoid the situation of
a tie.
- Distance metrics:
Minkowski distance is a generalized distance
metrics. It has the following metrics as
special cases.
- Maximum distance
- Euclidean distance
- City block distance
(also known as Manhattan metric
or taxicab distance)
- Kernel and window estimators
- Adaptive decision boundaries
- Adaptive discriminant functions
- One-layer perceptron approach
- Minimum squared error discriminant functions
This page is maintained by
Jyh-Shing Roger Jang.
Comments and suggestions are welcome:
jang@cs.nthu.edu.tw.