The PPT presentation for Vector Quantization developed by fresh spar technologies for the SKCET , Coimbatore for the Internals that occured in College. It helps in many fields such as Machine Learning , Artificial Intelligence etc... Social Links: Linkedin: https://www.linkedin.com/in/manojkumar--c/ instagram: https://www.instagram.com/manojkumar._.c/ Youtube: https://www.youtube.com/@Manojkumar_C/ Hashtags: #fresh_spar_technologies #fresh_spar #spyder_templates #manojkumar_c #startup #business

1. Learning Vector
Quantization…
Manojkumar C

2. Machine Learning vs Deep Learning :

3. What is Neural Network ?
Neural Networks use the architecture of human neurons which have multiple inputs, a processing
unit, and single/multiple outputs

4. Types of Neural Networks:
● Perceptron
● Feed Forward Neural Network
● Multilayer Perceptron
● Convolutional Neural Network
● Radial Basis Functional Neural Network
● Recurrent Neural Network
● LSTM – Long Short-Term Memory
● Sequence to Sequence Models
● Modular Neural Network

5. What is Learning Vector Quantization ?
Learning Vector Quantization ( or LVQ ) is a type of Artificial Neural Network which also inspired by
biological models of neural systems.
A Nearest Neighbour method, because the unknown vector from a set of reference vectors is sought
LVQ has two layers, one is the Input layer and the other one is the Output layer.

6. Algorithm of Learning Vector Quantization
1. Weight initialization
2. For 1 to N number of epochs
3. Select a training example
4. Compute the winning vector
5. Update the winning vector
6. Repeat steps 3, 4, 5 for all training example.
7. Classify test sample

7. What LVQ does ?
The LVQ algorithm allows one to choose the number of training instances to
undergo and then learns about what those instances look like.
LVQ is a prototype-based supervised classification algorithm. LVQ is
the supervised counterpart of vector quantization systems.

8. Uses of the Vector Quantization :
❖ Lossy data compression
❖ Lossy data correction
❖ Pattern recognition
❖ Density estimation and clustering
❖ Mainly in biometric modalities like fingerprinting, pattern
recognition, face recognition using codebooks of desired size

9. THANK YOU