LEADER 05282nam 22006975 450 001 9910483117403321 005 20200706013906.0 010 $a3-030-02384-2 024 7 $a10.1007/978-3-030-02384-3 035 $a(CKB)4100000007110693 035 $a(DE-He213)978-3-030-02384-3 035 $a(MiAaPQ)EBC5925744 035 $a(PPN)243768966 035 $a(EXLCZ)994100000007110693 100 $a20181112d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmpirical Approach to Machine Learning /$fby Plamen P. Angelov, Xiaowei Gu 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XXIX, 423 p. 139 illus., 90 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v800 300 $aIncludes Index. 311 $a3-030-02383-4 327 $aIntroduction -- Part I: Theoretical Background -- Brief Introduction to Statistical Machine Learning -- Brief Introduction to Computational Intelligence -- Part II: Theoretical Fundamentals of the Proposed Approach -- Empirical Approach - Introduction -- Empirical Fuzzy Sets and Systems -- Anomaly Detection - Empirical Approach -- Data Partitioning - Empirical Approach -- Autonomous Learning Multi-Model Systems -- Transparent Deep Rule-Based Classifiers -- Part III: Applications of the Proposed Approach -- Applications of Autonomous Anomaly Detection. 330 $aThis book provides a ?one-stop source? for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today?s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA: ?The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.? Paul J. Werbos, Inventor of the back-propagation method, USA: ?I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.? Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: ?This new book will set up a milestone for the modern intelligent systems.? Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: ?Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.?. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v800 606 $aComputational intelligence 606 $aPattern recognition 606 $aBig data 606 $aData mining 606 $aComputational complexity 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aComplexity$3https://scigraph.springernature.com/ontologies/product-market-codes/T11022 615 0$aComputational intelligence. 615 0$aPattern recognition. 615 0$aBig data. 615 0$aData mining. 615 0$aComputational complexity. 615 14$aComputational Intelligence. 615 24$aPattern Recognition. 615 24$aBig Data. 615 24$aData Mining and Knowledge Discovery. 615 24$aComplexity. 676 $a006.3 676 $a006.31 700 $aAngelov$b Plamen P$4aut$4http://id.loc.gov/vocabulary/relators/aut$0845344 702 $aGu$b Xiaowei$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483117403321 996 $aEmpirical Approach to Machine Learning$92854403 997 $aUNINA