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Support vector machines: a tool for pattern recognition and classification

Tchangani, Ayeley Support vector machines: a tool for pattern recognition and classification. (2005) Studies in Informatics and Control, 14 (2). 99-110. ISSN 1220-1766

(Document in English)

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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.

Item Type:Article
HAL Id:hal-02134723
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
South African National Research Foundation - NRF (SOUTH AFRICA)
Deposited On:25 Apr 2019 10:00

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