The power of computation and large memory of computers nowadays offer a great opportunity for information processing and storage. But information is not knowledge and one needs methods that permit to go from information to knowledge. Extracting automatically knowledge from storage data becomes then one of great challenge for the Information Technology (IT) industry. Pattern Recognition (PR) is the study of how machines can observe the environment, learn to distinguish pattern of interest from their background and make sound and reasonable decisions about the category of the pattern. The automatic recognition, classification, description, grouping of pattern is an important problem in engineering and sciences such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, remote sensing, manufacturing, etc. Computer programs that help many professional in their daily work such as doctors diagnosing disease, policemen identifying suspects, engineers supervising manufacturing plants and energy production systems, etc. depend in some way on pattern recognition. One important field and goal of pattern recognition is classification: supervised or unsupervised also known as clustering. In this paper we present a mathematical tool named support vector machines (SVM) that permit to derive efficient algorithms of learning and classification.
Pattern Recognition, Classification, Support Vector Machines, Supervised Learning, Mathematical (Quadratic) Programming
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).