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# Pattern Recognition

## Feature Extraction and Feature Selection

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 …

## 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 …

## 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 …

## 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 …

## 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 …

## 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 …

## 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 …

## 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 …