Departmental Elective CS- 503 (B) Pattern Recognition (last time revision)

January 20, 2026

✅ UNIT – I : Introduction to Pattern Recognition

🔹 What is Pattern Recognition?

Pattern Recognition means identifying patterns or regularities in data.

Example:

Face recognition in phones
Email spam detection
Handwritten digit recognition

🔹 Data Sets for Pattern Recognition

A dataset is a collection of data used to train or test a model.

Example:

Images of handwritten numbers (0–9)
Medical data of patients (age, BP, sugar)

🔹 Application Areas

Medical diagnosis
Speech recognition
Image processing
Fraud detection
Recommendation systems

🔹 Design Principles of a Pattern Recognition System

Basic steps:

Data collection
Feature extraction
Classifier design
Decision making

🔹 Classification vs Clustering

Classification → Data is labeled
Clustering → Data is unlabeled

Example:

Classification: Email → Spam / Not Spam
Clustering: Grouping customers by behavior

🔹 Supervised Learning

Data has input + correct output
Teacher is present

Example:
Training a model with labeled images of cats and dogs

🔹 Unsupervised Learning

No labels
Model finds patterns itself

Example:
Grouping students based on marks automatically

🔹 Decision Boundary & Decision Region

Decision Boundary: Line separating classes
Decision Region: Area belonging to one class

🔹 Metric Spaces & Distance

Distance tells how similar two data points are

Common distances:

Euclidean distance
Manhattan distance

✅ UNIT – II : Classification Techniques

🔹 What is Classification?

Assigning data to a known category.

🔹 Applications of Classification

Disease prediction
Spam filtering
Credit scoring

🔹 Types of Classification

Binary (Yes / No)
Multi-class (0–9 digits)

🔹 Decision Tree

Tree-like structure
Easy to understand

Example:
If marks > 60 → Pass else Fail

🔹 Naïve Bayes

Based on probability
Assumes features are independent

Example:
Spam email detection

🔹 Logistic Regression

Used for binary classification
Output is probability (0 or 1)

🔹 Support Vector Machine (SVM)

Finds best separating line
Maximizes margin

Example:
Separating spam and non-spam emails

🔹 Random Forest

Group of decision trees
More accurate than single tree

🔹 K-Nearest Neighbour (KNN)

Based on nearest data points

Example:
If most nearby students passed → You pass

🔹 Training Set & Test Set

Training set: Used to learn
Test set: Used to evaluate

🔹 Standardization & Normalization

Scaling data
Improves accuracy

✅ UNIT – III : Clustering & Paradigms

🔹 Paradigms of Pattern Recognition

Different ways to solve PR problems:

Statistical
Structural
Neural-based

🔹 Pattern & Class Representation

Pattern → Data instance
Class → Category

🔹 Criterion Function for Clustering

Measure to check how good clustering is

🔹 K-Means Clustering

Divides data into K groups
Iterative method

Example:
Grouping customers into 3 types

🔹 Hierarchical Clustering

Tree-like clustering
No need to choose K initially

🔹 Cluster Validation

Checks clustering quality

✅ UNIT – IV : Feature Extraction & Selection

🔹 Feature Extraction

Transform raw data into useful features

Example:
Edges from images

🔹 Feature Selection

Selecting best features, removing unnecessary ones

🔹 Uses

Reduces complexity
Improves accuracy
Faster processing

🔹 Algorithms

Branch and Bound
Sequential Forward Selection
Sequential Backward Selection
(l, r) Algorithm

✅ UNIT – V : Recent Advances in Pattern Recognition

🔹 Structural Pattern Recognition

Uses structure and relationships

🔹 Soft Computing

Fuzzy logic
Neural networks

🔹 Neuro-Fuzzy Systems

Combination of neural + fuzzy logic

🔹 Fuzzy Classification

One data point can belong to multiple classes

Example:
Temperature → Warm & Hot

🔹 Histogram Rule

Uses frequency of data

🔹 Density Estimation

Estimates data distribution

🔹 Nearest Neighbor Rule

Assigns class of nearest sample