Abstract

Pattern recognition is the act of taking in raw data and taking an action based on properties of the pattern. Recognizing visual patterns is a crucial part of our lives: we recognize people when we talk to them, we recognize our cup on the breakfast table, our car in a parking lot, and so on. While this task is performed with great accuracy and apparent little effort by humans, it is still unclear how this performance is achieved. This has challenged the computer vision and machine learning research community to build artificial systems able to reproduce the human performance. After 30 years of intensive research, the challenge is still open.
Support Vector Machines (SVMs) are a new generation learning system based on recent advances in statistical learning theory. SVMs deliver stateoftheart performance in realworld applications such as text categorization, handwritten character recognition, image classification, biosequence analysis, and so on. Their first introduction in the early 90s led to an explosion of applications and deepening theoretical analysis, that has now established SVMs as one of the standard tools for machine learning.
The goal of the course is to enable interested researchers and students to use SVMs and SVMbased algorithms for stateoftheart visual application. I will not assume any prior knowledge on SVMs and or visual pattern recognition, but I will assume knowledge of probability theory.