Pattern Recognition and Application - Study24x7

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# Pattern Recognition and Application

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Pattern recognition is the automated recognition of patterns and regularities in data. Pattern recognition is closely related to artificial intelligence and machine learning,[1] together with applications such as data mining and knowledge discovery in databases (KDD), and is often used interchangeably with these terms. However, these are distinguished: machine learning is one approach to pattern recognition, while other approaches include hand-crafted (not learned) rules or heuristics; and pattern recognition is one approach to artificial intelligence, while other approaches include symbolic artificial intelligence. A modern definition of pattern recognition is: The field of pattern recognition is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories.

• Total 25 Modules
• 40 Videos
• Published on 29 June, 2019

• Introduction

59m

## Feature Extraction - I

• Feature Extraction - I

59m

## Feature Extraction - II

• Feature Extraction - II

59m

## Feature Extraction - III

• Feature Extraction - III

56m

## Bayes Decision Theory

• Bayes Decision Theory

57m
• Bayes Decision Theory (Contd.)

58m

## Normal Density and Discriminant Function

• Normal Density and Discriminant Function

52m
• Normal Density and Discriminant Function (Contd.)

58m

## Bayes Decision Theory - Binary Features

• Bayes Decision Theory - Binary Features

51m

## Maximum Likelihood Estimation

• Maximum Likelihood Estimation

54m

## Probability Density Estimation

• Probability Density Estimation

56m
• Probability Density Estimation (Contd.)

58m
• Probability Density Estimation (Contd. )

53m
• Probability Density Estimation ( Contd.)

56m
• Probability Density Estimation ( Contd. )

57m

## Dimensionality Problem

• Dimensionality Problem

57m

## Multiple Discriminant Analysis

• Multiple Discriminant Analysis

57m
• Multiple Discriminant Analysis (Tutorial)

54m
• Multiple Discriminant Analysis (Tutorial )

53m

## Perceptron Criterion

• Perceptron Criterion

54m
• Perceptron Criterion (Contd.)

54m

## MSE Criterion

• MSE Criterion

54m

## Linear Discriminator (Tutorial)

• Linear Discriminator (Tutorial)

58m

## Neural Networks for Pattern Recognition

• Neural Networks for Pattern Recognition

6m
• Neural Networks for Pattern Recognition (Contd.)

58m
• Neural Networks for Pattern Recognition (Contd. )

52m

## RBF Neural Network

• RBF Neural Network

57m
• RBF Neural Network (Contd.)

53m

## Support Vector Machine

• Support Vector Machine

55m

## Hyperbox Classifier

• Hyperbox Classifier

53m
• Hyperbox Classifier (Contd.)

56m

## Fuzzy Min Max Neural Network for Pattern Recognition

• Fuzzy Min Max Neural Network for Pattern Recognition

55m

## Reflex Fuzzy Min Max Neural Network

• Reflex Fuzzy Min Max Neural Network

54m

## Unsupervised Learning -Clustering

• Unsupervised Learning - Clustering

53m

## Clustering (Contd.)

• Clustering (Contd.)

52m

## Clustering using minimal spanning tree

• Clustering using minimal spanning tree

56m

## Temporal Pattern Recognition

• Temporal Pattern recognition

56m

## Hidden Markov Model

• Hidden Markov Model

55m
• Hidden Markov Model (Contd.)

59m
• Hidden Markov Model (Contd. )

59m

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