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Pattern Recognition and Application

Prof P K Biswas

3 followers

Pattern Recognition and Application

1 Learner

Pattern Recognition and Application

Validity Unlimited Pattern Recognition and Application All Level Pattern Recognition and Application English

0.0 (0)
Free

About This Course

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.

  • Pattern Recognition and Application Total 25 Modules
  • Pattern Recognition and Application 40 Videos
  • Pattern Recognition and Application Downloadable on mobile
  • Pattern Recognition and Application Published on 29 June, 2019

Course Curriculum

Introduction

Pattern Recognition and Application
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    Introduction

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Feature Extraction - I

Pattern Recognition and Application
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    Feature Extraction - I

    59m <?php echo  $alt; ?>

Feature Extraction - II

Pattern Recognition and Application
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    Feature Extraction - II

    59m <?php echo  $alt; ?>

Feature Extraction - III

Pattern Recognition and Application
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    Feature Extraction - III

    56m <?php echo  $alt; ?>

Bayes Decision Theory

Pattern Recognition and Application
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    Bayes Decision Theory

    57m <?php echo  $alt; ?>
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    Bayes Decision Theory (Contd.)

    58m <?php echo  $alt; ?>

Normal Density and Discriminant Function

Pattern Recognition and Application
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    Normal Density and Discriminant Function

    52m <?php echo  $alt; ?>
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    Normal Density and Discriminant Function (Contd.)

    58m <?php echo  $alt; ?>

Bayes Decision Theory - Binary Features

Pattern Recognition and Application
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    Bayes Decision Theory - Binary Features

    51m <?php echo  $alt; ?>

Maximum Likelihood Estimation

Pattern Recognition and Application
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    Maximum Likelihood Estimation

    54m <?php echo  $alt; ?>

Probability Density Estimation

Pattern Recognition and Application
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    Probability Density Estimation

    56m <?php echo  $alt; ?>
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    Probability Density Estimation (Contd.)

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    Probability Density Estimation (Contd. )

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    Probability Density Estimation ( Contd.)

    56m <?php echo  $alt; ?>
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    Probability Density Estimation ( Contd. )

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Dimensionality Problem

Pattern Recognition and Application
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    Dimensionality Problem

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Multiple Discriminant Analysis

Pattern Recognition and Application
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    Multiple Discriminant Analysis

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    Multiple Discriminant Analysis (Tutorial)

    54m <?php echo  $alt; ?>
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    Multiple Discriminant Analysis (Tutorial )

    53m <?php echo  $alt; ?>

Perceptron Criterion

Pattern Recognition and Application
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    Perceptron Criterion

    54m <?php echo  $alt; ?>
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    Perceptron Criterion (Contd.)

    54m <?php echo  $alt; ?>

MSE Criterion

Pattern Recognition and Application
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    MSE Criterion

    54m <?php echo  $alt; ?>

Linear Discriminator (Tutorial)

Pattern Recognition and Application
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    Linear Discriminator (Tutorial)

    58m <?php echo  $alt; ?>

Neural Networks for Pattern Recognition

Pattern Recognition and Application
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    Neural Networks for Pattern Recognition

    6m <?php echo  $alt; ?>
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    Neural Networks for Pattern Recognition (Contd.)

    58m <?php echo  $alt; ?>
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    Neural Networks for Pattern Recognition (Contd. )

    52m <?php echo  $alt; ?>

RBF Neural Network

Pattern Recognition and Application
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    RBF Neural Network

    57m <?php echo  $alt; ?>
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    RBF Neural Network (Contd.)

    53m <?php echo  $alt; ?>

Support Vector Machine

Pattern Recognition and Application
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    Support Vector Machine

    55m <?php echo  $alt; ?>

Hyperbox Classifier

Pattern Recognition and Application
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    Hyperbox Classifier

    53m <?php echo  $alt; ?>
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    Hyperbox Classifier (Contd.)

    56m <?php echo  $alt; ?>

Fuzzy Min Max Neural Network for Pattern Recognition

Pattern Recognition and Application
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    Fuzzy Min Max Neural Network for Pattern Recognition

    55m <?php echo  $alt; ?>

Reflex Fuzzy Min Max Neural Network

Pattern Recognition and Application
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    Reflex Fuzzy Min Max Neural Network

    54m <?php echo  $alt; ?>

Unsupervised Learning -Clustering

Pattern Recognition and Application
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    Unsupervised Learning - Clustering

    53m <?php echo  $alt; ?>

Clustering (Contd.)

Pattern Recognition and Application
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    Clustering (Contd.)

    52m <?php echo  $alt; ?>

Clustering using minimal spanning tree

Pattern Recognition and Application
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    Clustering using minimal spanning tree

    56m <?php echo  $alt; ?>

Temporal Pattern Recognition

Pattern Recognition and Application
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    Temporal Pattern recognition

    56m <?php echo  $alt; ?>

Hidden Markov Model

Pattern Recognition and Application
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    Hidden Markov Model

    55m <?php echo  $alt; ?>
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    Hidden Markov Model (Contd.)

    59m <?php echo  $alt; ?>
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    Hidden Markov Model (Contd. )

    59m <?php echo  $alt; ?>

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