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Advances in Machine Learning and Data Analysis 2010 ed. [Kõva köide]

  • Formaat: Hardback, 240 pages, kõrgus x laius: 235x155 mm, kaal: 1160 g, VIII, 240 p., 1 Hardback
  • Sari: Lecture Notes in Electrical Engineering 48
  • Ilmumisaeg: 23-Nov-2009
  • Kirjastus: Springer
  • ISBN-10: 9048131766
  • ISBN-13: 9789048131761
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  • Formaat: Hardback, 240 pages, kõrgus x laius: 235x155 mm, kaal: 1160 g, VIII, 240 p., 1 Hardback
  • Sari: Lecture Notes in Electrical Engineering 48
  • Ilmumisaeg: 23-Nov-2009
  • Kirjastus: Springer
  • ISBN-10: 9048131766
  • ISBN-13: 9789048131761
A large international conference on Advances in Machine Learning and Data Analysis was held in UC Berkeley, California, USA, October 22-24, 2008, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2008). This volume contains sixteen 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. Advances in Machine Learning and Data Analysis offers the state of the art of tremendous advances in machine learning and data analysis and also serves as an excellent reference text for researchers and graduate students, working on machine learning and data analysis.

This volume contains sixteen revised and extended research articles presented at a large international conference on Advances in Machine Learning and Data Analysis held at Berkeley in 2008. They explore the state-of-the art over a wide array of topics.

Arvustused

From the reviews:

This is a collection of papers from a large international conference on advances in machine learning and data analysis . Readers who work with digital systems would benefit most from this book. Each chapter has a bibliography that helps readers find further references, when needed. the topics covered in this book should be of great interest to researchers and practitioners who want to apply machine learning technology and data analysis tools to problems in general electrical engineering areas . (Xiannong Meng, ACM Computing Reviews, March, 2010)

2D/3D Image Data Analysis for Object Tracking and Classification.- Robot
Competence Development by Constructive Learning.- Using Digital Watermarking
for Securing Next Generation Media Broadcasts.- A Reduced-Dimension Processor
Model.- Hybrid Machine Learning Model for Continuous Microarray Time Series.-
An Asymptotic Method to a Financial Optimization Problem.- Analytical Design
of Robust Multi-loop PI Controller for Multi-time Delay Processes.- Automatic
and Semi-automatic Methods for the Detection of Quasars in Sky Surveys.-
Improving Low-Cost Sail Simulator Results by Artificial Neural Networks
Models.- Rough Set Approaches to Unsupervised Neural Network Based Pattern
Classifier.- A New Robust Combined Method for Auto Exposure and Auto
White-Balance.- A Mathematical Analysis Around Capacitive Characteristics of
the Current of CSCT: Optimum Utilization of Capacitors of Harmonic Filters.-
Harmonic Analysis and Optimum Allocation of Filters in CSCT.- Digital Pen and
Paper Technology as a Means of Classroom Administration Relief.- A Conceptual
Model for a Network-Based Assessment Security System.- Incorrect Weighting of
Absolute Performance in Self-Assessment.