Feature Extraction and Feature Selection Feature Extraction Feature extraction is for creating a new smaller set of features that stills captures most of the useful information. Feature Selection Feature selection …

Read More »## Linear Discriminant Function and How to compute it?

Linear Discriminant functions Linear Discriminant functions are the basis for the majority of Pattern Recognition techniques. It is a function that maps input features onto a classification space. A dividing …

Read More »## Define Likelihood and Maximum Likelihood Method

Likelihood and Maximum Likelihood Method Likelihood Likelihood is the probability that an observation is predicted by the specified model. Maximum Likelihood Maximum likelihood is the maximum probability that an observation …

Read More »## Analog to Digital – A/D conversion steps and tools

Analog to Digital – A/D conversion steps The A/D conversion (coding) involves: Sampling: measuring the amplitude values (or function values) at a finite number of positions. Quantization: representing the amplitude values …

Read More »## Analog to Digital Conversion – needs for A/D conversion

Analog to Digital Conversion – needs for A/D conversion A/D conversion is a sampling from the perspective of Pattern Recognition which goal is to gather sensed data from samples and …

Read More »## Data Reduction – Classification and need of Data Reduction

Data Reduction is the transformation of data into a corrupted, ordered and simplified form. Classification of Data Reduction techniques Data cube aggregation ➢ Aggregation operations are applied to the data …

Read More »## Dimension Reduction Dimensionalily Reduction

Dimension Reduction Dimensionalily Reduction A dimension denotes a measurement of a certain aspect of an object. Other names for dimension are attribute or feature or variable names in a data …

Read More »## Pattern Recognition tasks and examples

Pattern Recognition tasks and examples Two types of pattern recognition tasks are available. Supervised Pattern Recognition: If training data is available and the model has prior known information. Examples: Fingerprint …

Read More »## Why Naive Bayes Classifier is special?

Naive Bayes Classifier is special Naive Bayes Classifier are a family of simple ‘Probabilistic Classifier’ based on applying Bayes Theorem with strong (naive) independence assumptions between the features. It is …

Read More »## Gaussian Distribution applications

Gaussian Distribution applications Gaussian functions are broadly used in image processing Gaussian distribution is used for various signal processing Gaussian functions are used to define Artificial Neural Network (ANN) Gaussian …

Read More »