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020 _a9783030046637
024 7 _a10.1007/978-3-030-04663-7
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQA248.5
_b2019 EB
100 1 _aVluymans, Sarah.
_eautor
_9101794
245 1 0 _aDealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods
_cby Sarah Vluymans
264 1 _aCham
_bSpringer International Publishing :
_bImprint: Springer
_c2019
300 _a1 recurso en línea (XVIII, 249 páginas)
_b23 ilustraciones, 10 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aStudies in Computational Intelligence
_x1860-949X
_v807
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aIntroduction -- Classification -- Understanding OWA based fuzzy rough sets -- Fuzzy rough set based classification of semi-supervised data -- Multi-instance learning -- Multi-label learning -- Conclusions and future work -- Bibliography.
520 3 _aThis book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data. The book makes two main contributions: helping readers gain a deeper understanding of the underlying mathematical theory; and developing new, intuitive and well-performing classification approaches. The authors bridge the gap between the theoretical proposals of the mathematical model and important challenges in machine learning. The intended readership of this book includes anyone interested in learning more about fuzzy rough set theory and how to use it in practical machine learning contexts. Although the core audience chiefly consists of mathematicians, computer scientists and engineers, the content will also be interesting and accessible to students and professionals from a range of other fields.
988 _aPrimersemestre_2019_Robotics
650 7 _2embne
_aConjuntos difusos
_9145903
650 7 _2embne
_aAprendizaje automático
_9166090
650 7 _2embne
_aConjuntos, Teoría de
_9405124
776 0 8 _iPrinted edition:
_z9783030046620
776 0 8 _iPrinted edition:
_z9783030046644
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-04663-7
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _aSI
_cm
_dz
_feng
_ggw
_h0
_b10/2019
_eel
_zSI