Statistical Pattern Recognition
- (Stanford University - Alvin Wei-Cheng Wong)
- Overview
Statistical pattern recognition (SPR) is a data analysis field that uses mathematical models and algorithms to identify patterns from large datasets.
Pattern recognition is a data analysis method that uses machine learning algorithms to automatically recognize patterns and regularities in data. This data can be anything from text and images to sounds or other definable qualities.
The goal of SPR is to collect observations, study them, and infer general rules or concepts that can be applied to new observations. The main goal in developing a pattern recognition system is to make the error as small as possible.
Some examples of pattern recognition applications include:
- Image recognition: Identifying faces in photographs, object recognition and classification, identifying landmarks, and detecting body poses
- Video recognition: Identifying people, intrusion detection, motion recognition, real-time object detection, and object tracking
SPR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning.
[More to come ...]
