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Pattern Recognition

Pattern recognition is the process of designing and developing for recognizing patterns by analyzing scene from real world and providing description of scene as a result for the completion of a task. It extracts significant features or attributes from background irrelevant detailed data and categorized the input data into identifiable classes.

It deals with the description and analyses of measurements taken from physical or mental processes by acquiring raw data and taking actions based on the “class” of the patterns recognized in the data. Pattern recognition deals with the choice of sensors, pre-processing techniques, representation scheme, and decision making model.

Few Topics are: 

• Bayesian Decision Theory ,Bayesian Parameter estimation ,non Parametric Methods ,Linear Discrimination ,Multilayer neural network
• Stochastic methods,search boltzman machines,Genetic algorithms,vector quantization,Nonmetric Methods ,unsupervised learning
• clustering, Dimensionality Reduction ,Hidden Markov Models ,TBD Deep Learning,Feature Selection,Dimensionality Reduction
• Deep Learning,Support Vector Machines,Text Analysis, Distribution free classification, discriminant functions, training algorithms, 
• Mixture Models,Ensemble Methods,Competitive Learning,Linear Discriminant Functions,Connectionism ,Neural Networks, 
• recognizing patterns in images, signals,Bayes decision theory,supervised learning,nonparametric techniques,artificial neural networks
• statistical classification, parametric and nonparametric techniques

 Complex topics :

• Bayesian Decision Theory
  
• Bayesian Networks
  
• Maximum Likelihood Estimation
  
• Bayesian Estimation
  
• Dimensionality Reduction
  
• Feature Selection
  
• Linear Discriminant Functions
  
• Support Vector Machines (SVMs)
  
• Expectation-Maximization (EM) Algorithm
  
• Non-parametric Estimation

Some Other Topics are:

• statistical pattern recognition (PR) algorithms
  
• Decision theory
  
• Parameter estimation
  
• Density estimation
  
• Non-parametric techniques,
  
• Supervised learning
  
• Dimensionality reduction
  
• Linear discriminant functions
  
• Clustering
  
• Unsupervised learning
  
• Feature extraction and Applications