Computational methods for the identification and classification of objects. Feature extraction, feature-space representation, distance and similarity measures, decision rules. Supervised and unsupervised learning. Statistical pattern recognition: multivariate random variables; Bayes and minimum-risk decision theory; probability or error; feature reduction and principal components analysis; parametric and nonparametric methods; clustering; hierarchical systems. Syntactic pattern recognition: review of automata and language theory; shape descriptors; syntactic recognition systems; grammatical inference and learning. Artificial neural networks as recognition systems.
Pattern recognition is important in many fields related to electrical and computer engineering, including signal analysis, image analysis, and communication theory.
The course assumes knowledge of probability and random variables, as introduced in STAT 4714.
Percentage of Course
|Review of Statistical Methods||20%|
|Linear and Piecewise-Linear Discriminate Design||15%|
|Review of Automata Theory and Formal Languages||15%|
|Grammatical Inference; Learning in Syntactic Recognition||20%|
|Recognition using Artificial Neural Networks||15%|