Pattern Recognition II


Training pattern recognition systems: Line search, gradient descent, Conjugate gradients, Newton, the Levenberg-Marquart algorithm, Bayes learning, Monte Carlo methods, simulated annealing, Genetic algorithms. Minimum description length principle. Preprocessing and feature selection. KarhunenLeone expansion. Syntactic pattern recognition and error correction. Markov and hidden Markov models, recurrent neural networks and non-linear temporal processing. Image recognition applications.

Objectives

The student upon successful completion of the course, expected to know:

Prerequisites

1.Pattern Recognition Ι

Syllabus

The contents of the course Pattern Recognition I are: 1. Pattern Recognition Systems Training 2. Time-Variant Patterns

COURSE DETAILS

Level:

Type:

Undergraduate

(A-)

Instructors: Evangelos Dermatas
Department: Electrical and Computer Engineering
Institution: University of Patras
Subject: Science in Electrical Engineering
Rights: CC - Attribution

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