Pattern recognition and classification is the act of taking in raw data and using a set of properties and features take an action on the data.

1. Design Cycles of
Pattern Recognition
Al Mamun Khan
1

2. Overview
 Design Circle
 Data collection
 Feature Choice
 Model Choice
 Training
 Evaluation
2

3. Collect Data
Choose Features
Choose Model
Train Classifier
Evaluate Classifier
 Design Circle

4. Collect
Data
Choose
Features
Choose
Model
Train
Classifier
Evaluate
Classifier
 Data Collection
Data Collection
 Collecting training and testing
data
 How can we know when we
have adequately large and
representative set of
samples?

5. Collect
Data
Choose
Features
Choose
Model
Train
Classifier
Evaluate
Classifier
 Features
Choose Features
 Depends on
characteristics of problem
domain
 Ideally
 Simple to extract
 Invariant to irrelevant
transformation
 Insensitive to noise

6. Collect
Data
Choose
Features
Choose
Model
Train
Classifier
Evaluate
Classifier
 Model
Choose Model
 Domain dependence and
prior information.
 Definition of design criteria.
 Parametric vs. non-
parametric models.
 Handling of missing
features.
 Computational complexity.

8. Collect
Data
Choose
Features
Choose
Model
Train
Classifier
Evaluate
Classifier
 Training
Train Classifier
 How can we learn the rule from
data?
 Supervised learning
 Unsupervised learning
 Reinforcement learning: no
desired category is given but the
teacher provides feedback to the
system such as the decision is
right or wrong.

9. Collect
Data
Choose
Features
Choose
Model
Train
Classifier
Evaluate
Classifier
 Evaluate Classifier
Evaluate Classifier
 How can we estimate the
performance with training
samples?
 How can we predict the
performance with future data?
 Problems of overfitting and
generalization.