Pattern Recognition

EM- Expectation maximization algorithm and applications EM algorithm is an iterative method for finding maximum likelihood or Maximum A Posteriri (MAP) extimates of parameters in statistical models where the models …

Viterbi algorithm and applications Viterbi algorithm is a dynamic programming algorithm that is used to find the most likely state transition in a state diagram by giving a noisy sequence …

Fuzzy logic vs Boolean logic Fuzzy logic is the superset of boolean logic whereas boolean logic is the subset of fuzzy logic. Boolean sets have two valued logic, true or …

Fuzzy Logic – its characteristics and application Fuzzy logic is form of many valued logic or probabilistic logic that deals with reasoning that is approximate rather than fixed and exact …

Pattern Recognition pipeline Pattern recognition pipeline is described below. There are two phases on pattern recognition pipeline. These are classification phase and learning phase. Classification phase: i. image acquisition ii. …

The Hopefield Neural Network The Hopefield Neural Network is the simplest Artificial Neural Network (ANN) that is fully connected single layer auto associative network that has one single layer with …

Kohonen Neural Network is an Artificial Neural Network (ANN) that does not use activation function and bias weight and hidden layer and is trained in an unsupervised mode and only applicable …

Learning is a phenomena through which a system gets trained and becomes adaptable to give result in a accurate manner. Training Set: A set of data that is used to …

Feature is a measurement taken as the input patterns that is to be classified and it may be represented as continuous, discrete and binary values. A feature vector is just …

Pattern recognition is a branch of artificial intelligence and the research area that studies the operation and design of systems that recognize patterns in data. The aim is to classify …