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This book provides a comprehensive and systematic introduction to
the principal machine learning methods, covering both supervised
and unsupervised learning methods. It discusses essential methods
of classification and regression in supervised learning, such as
decision trees, perceptrons, support vector machines, maximum
entropy models, logistic regression models and multiclass
classification, as well as methods applied in supervised learning,
like the hidden Markov model and conditional random fields. In the
context of unsupervised learning, it examines clustering and other
problems as well as methods such as singular value decomposition,
principal component analysis and latent semantic analysis. As a
fundamental book on machine learning, it addresses the needs of
researchers and students who apply machine learning as an important
tool in their research, especially those in fields such as
information retrieval, natural language processing and text data
mining. In order to understand the concepts and methods discussed,
readers are expected to have an elementary knowledge of advanced
mathematics, linear algebra and probability statistics. The
detailed explanations of basic principles, underlying concepts and
algorithms enable readers to grasp basic techniques, while the
rigorous mathematical derivations and specific examples included
offer valuable insights into machine learning.
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Brain Informatics - International Conference, BI 2009, Beijing, China, October 22-24, Proceedings (Paperback, 2009 ed.)
Ning Zhong, Kuncheng Li, Shengfu Lu, Lin Chen
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R1,543
Discovery Miles 15 430
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Ships in 10 - 15 working days
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This volume contains the papers selected for presentation at The
2009 Inter- tional Conference on Brain Informatics (BI 2009) held
at Beijing University of Technology, China, on October 22-24, 2009.
It was organized by the Web Int- ligence Consortium (WIC) and IEEE
Computational Intelligence Society Task Force on Brain Informatics
(IEEE TF-BI). The conference was held jointly with The 2009
International Conference on Active Media Technology (AMT 2009).
Brain informatics (BI) has emergedas an
interdisciplinaryresearch?eld that focuses on studying the
mechanisms underlying the human information proce- ing system
(HIPS). It investigates the essential functions of the brain,
ranging from perception to thinking, and encompassing such areas as
multi-perception,
attention,memory,language,computation,heuristicsearch,reasoning,planning,
decision-making, problem-solving, learning, discovery, and
creativity. The goal of BI is to develop and demonstrate a
systematic approach to achieving an integrated understanding of
both macroscopic and microscopic level working principles of the
brain, by means of experimental, computational, and cognitive
neuroscience studies, as well as utilizing advanced Web
Intelligence (WI) centric information technologies. BI represents a
potentially revolutionary shift in the way that research is
undertaken. It attempts to capture new forms of c- laborative and
interdisciplinary work. Following this vision, new kinds of BI
methods and global research communities will emerge, through
infrastructure on the wisdom Web and knowledge grids that enables
high speed and d- tributed, large-scale analysis and computations,
and radically new ways of sh- ing data/knowledge.
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