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State, Luminita
Bucharest University
Bucharest, Romania
 
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   Feature selection for data and pattern recognition
Stanczyk U., Jain L., Springer Publishing Company, Incorporated, New York, NY, 2014. 355 pp.  Type: Book (978-3-662456-19-4)

Feature selection is one of the most important preprocessing steps, with the performance of any system designed to solve pattern recognition, or data mining tasks in general, being strongly dependent on the quality of the feature set i...

Jun 16 2015  
   Partitional clustering algorithms
Celebi M., Springer Publishing Company, Incorporated, New York, NY, 2014. 415 pp.  Type: Book (978-3-319092-58-4)

Given the wide range of their major applications, during the past decade, unsupervised classification techniques (also referred to as clustering) have received special attention from many scientists, yielding new classes of algorithms...

Apr 13 2015  
   Support vector machines applications
Ma Y., Guo G., Springer Publishing Company, Incorporated, New York, NY, 2014. 308 pp.  Type: Book (978-3-319022-99-4)

Although support vector machines (SVMs) are a relatively new topic in the field of machine learning, there have been many papers and books published that report on the progress of both theoretical research and practical developments. T...

Aug 18 2014  
   Multilayer neural networks: a generalized net perspective
Krawczak M., Springer Publishing Company, Incorporated, New York, NY, 2013. 194 pp.  Type: Book (978-3-319002-47-7)

While many books have been published on multilayer neural networks, this book distinguishes itself with new insights on the structure and functioning of these systems, stated in terms of more general concepts and methods coming from th...

Apr 16 2014  
   Optimization for machine learning
Sra S., Nowozin S., Wright S., The MIT Press, Cambridge, MA, 2011. 512 pp.  Type: Book (978-0-262016-46-9), Reviews: (2 of 2)

Machine learning is a natural outgrowth of the intersection of computer science, statistics, mathematics, and engineering, its multidisciplinary nature being underscored by a long series of applications in a wide class of areas from fi...

Apr 9 2012  
   A first course in numerical methods
Ascher U., Greif C., Society for Industrial and Applied Mathematics, Philadelphia, PA, 2011. 574 pp.  Type: Book (978-0-898719-97-0)

Most engineering applications, in a wide range of domains, are based on mathematically founded methods and algorithms. This excellent introductory textbook covers numerical methods that are currently used to model and solve engineering...

Dec 29 2011  
   Recurrent neural networks for fuzzy data
Freitag S., Graf W., Kaliske M. Integrated Computer-Aided Engineering 18(3): 265-280, 2011.  Type: Article

This paper proposes a new model-free approach for discovering and predicting the hidden input/output dependencies of a system when both the input and output spaces are viewed as imprecise environments. The imprecision in data is modele...

Dec 2 2011  
   Bayesian artificial intelligence (2nd ed.)
Korb K., Nicholson A., CRC Press, Inc., Boca Raton, FL, 2010. 491 pp.  Type: Book (978-1-439815-91-5)

This second edition includes new information and some minor error corrections to the previous edition [1]. The content is structured into three parts devoted to probabilistic reasoning, learning causal models, and knowledge engineering...

Oct 20 2011  
   Relational data clustering: models, algorithms, and applications
Long B., Zhang Z., Yu P., Chapman & Hall/CRC, Boca Raton, FL, 2010. 216 pp.  Type: Book (978-1-420072-61-7)

To date, numerous proposed clustering methods have been published in a long series of papers and included in several outstanding books. This book, however, as the authors say, is the first monograph on the topic of relational data clus...

Jun 13 2011  
   Evolutionary clustering of relational data
Horta D., Campello R. International Journal of Hybrid Intelligent Systems 7(4): 261-281, 2010.  Type: Article

A previous paper by the authors introduced the basics of fast evolutionary algorithms for relational data and proposed a new asymptotic complexity analysis of the algorithms in terms of running time. This paper is an extension of that ...

Apr 11 2011  
 
 
 
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