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020 _a9783319975474
024 7 _a10.1007/978-3-319-97547-4
_2doi
040 _bspa
_dES-MaUEC
_cES-MaUEC
050 4 _aQA76.9.B45
_b2019 EB
245 0 0 _aUncertainty Modelling in Data Science
_cedited by Sébastien Destercke, Thierry Denoeux, María Ángeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz.
264 1 _aCham
_bImprint: Springer
_c2019
300 _a 1 recurso en línea (XI, 234 páginas)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
_2rda
490 0 _aAdvances in Intelligent Systems and Computing
_x2194-5357
_v832
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
505 0 _aChapter 1. Imprecise statistical inference for accelerated life testing data: imprecision related to the log-rank test (Abdullah Ahmadini) -- Chapter 2. Descriptive comparison of the rating scales through different scale estimates. Simulation-based analysis (Irene Arellano) -- Chapter 3. Central Moments of a Fuzzy Random Variable using the Signed Distance: a Look towards the Variance (Redina Berkachy) -- Chapter 4. On Missing Membership Degrees: Modelling Non-existence, Ignorance and Inconsistency (Michal Burda) etc.
520 3 _aThis book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17-21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair. Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs. The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them. Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.
988 _aPrimersemestre_2019_Robotics
650 7 _2embne
_9495511
_aDatos masivos
650 7 _2embne
_aInteligencia artificial
_9413115
700 1 _aDestercke, Sébastien.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDenoeux, Thierry.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGil, María Ángeles.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGrzegorzewski, Przemyslaw.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_998793
700 1 _aHryniewicz, Olgierd.
_eeditor literario
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
776 0 8 _iPrinted edition:
_z9783319975467
776 0 8 _iPrinted edition:
_z9783319975481
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-97547-4
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
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998 _aSI
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