TY - BOOK ID - 5454176 TI - Rule extraction from support vector machines PY - 2008 VL - 80 SN - 9783540753896 3540753893 3540753907 PB - Berlin ; Heidelberg : Springer-Verlag, DB - UniCat KW - Machine learning KW - Apprentissage automatique KW - Support vector machines KW - Applied Mathematics KW - Computer Science KW - Civil Engineering KW - Engineering & Applied Sciences KW - Civil & Environmental Engineering KW - Machine learning. KW - Computational learning theory. KW - Learning, Machine KW - Engineering. KW - Artificial intelligence. KW - Applied mathematics. KW - Engineering mathematics. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Artificial Intelligence (incl. Robotics). KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Mathematics KW - Artificial intelligence KW - Mathematical and Computational Engineering. KW - Artificial Intelligence. KW - Support vector machines. KW - SVMs (Algorithms) KW - Algorithms KW - Kernel functions KW - Supervised learning (Machine learning) UR - https://www.unicat.be/uniCat?func=search&query=sysid:5454176 AB - Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts. ER -