A Course on PATTERN RECOGNITION Sergios Theodoridis KonstantinosKoutroumbas Version 3 日
1 Sergios Theodoridis Konstantinos Koutroumbas Version 3
PATTERN RECOGNITON typical application areas Machine vision Character recognition(OCR) Computer aided diagnosis Speech /Music/Audio recognition Face recognition Biometrics Image data Base retrieval Data mining Social Networks Bionformatics The task: Assign unknown objects- patterns -into the correct cass. This is known as classification
2 PATTERN RECOGNITION ❖ Typical application areas ➢ Machine vision ➢ Character recognition (OCR) ➢ Computer aided diagnosis ➢ Speech/Music/Audio recognition ➢ Face recognition ➢ Biometrics ➢ Image Data Base retrieval ➢ Data mining ➢ Social Networks ➢ Bionformatics ❖ The task: Assign unknown objects – patterns – into the correct class. This is known as classification
Features: These are measurable quantities obtained from the patterns and the classification task is based on their respective values. Feature vectors: a number of features X 15°l constitute the feature vector X=IX R Feature vectors are treated as random vectors
3 ❖ Features: These are measurable quantities obtained from the patterns, and the classification task is based on their respective values. ❖Feature vectors: A number of features constitute the feature vector Feature vectors are treated as random vectors. ,..., , 1 l x x T l x = x1 ,..., xl R
An example
4 An example:
. the classifier consists of a set of functions whose values computed at x, determine the class to which the corresponding pattern belongs Classification system overview Patterns sensor feature generation feature selection classifier design system L evaluation
5 ❖ The classifier consists of a set of functions, whose values, computed at , determine the class to which the corresponding pattern belongs ❖ Classification system overview x sensor feature generation feature selection classifier design system evaluation Patterns