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Machine Learning And Systems Engineering
Machine Learning And Systems Engineering

Machine Learning And Systems Engineering

Tác giả : Sio-Iong Ao – Burghard Rieger – Mahyar A. Amouzegar

Số trang : 635

Lượt xem : 789

Lượt tải : 147

Lượt đọc : 206

Kích thước : 11.28 MB

Đăng lúc : 1 năm trước

Thể loại :

AI Công Nghệ Thông Tin

Đọc trên điện thoại

Đọc Machine Learning And Systems Engineering trên điện thoại

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20–22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). The WCECS is organized by the International Association of Engineers (IAENG). IAENG is a non-profit international association for the engineers and the computer scientists, which was founded in 1968 and has been undergoing rapid expansions in recent years. The WCECS conferences have served as excellent venues for the engineering community to meet with each other and to exchange ideas. Moreover, WCECS continues to strike a balance between theoretical and application development. The conference committees have been formed with over two hundred members who are mainly research center heads, deans, department heads (chairs), professors, and research scientists from over thirty countries with the full committee list available at our congress web site (http:// www.iaeng.org/WCECS2009/committee.html). The conference participants are truly international representing high level research and development from many countries. The responses for the congress have been excellent. In 2009, we received more than six hundred manuscripts, and after a thorough peer review process 54.69% of the papers were accepted.

This volume contains 46 revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. The book offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

Sio-Iong Ao
Burghard B. Rieger
Mahyar A. Amouzegar

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