000 03043nam a22004095i 4500
999 _c362444
_d362444
001 362444
003 ES-MaUEC
005 20240111050219.0
006 a||||fo|||| 00| 0
007 cr nn 008mamaa
008 210607s2021 sz | s |||| 0|eng d
020 _a9783030755218
024 7 _a10.1007/978-3-030-75521-8
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aHG4515.5
_b2021 EB
100 _aRutkowski, Tom
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9682563
245 1 0 _aExplainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance
_cby Tom Rutkowski.
250 _aFirst edition 2021
264 1 _aCham
_bSpringer International Pulishing
_c2021
300 _a1 recurso en línea (XIX, 167 páginas)
_b118 ilustraciones, 72 ilustraciones a color
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _aarchivo de texto
_bPDF
490 0 _aStudies in Computational Intelligence
_x1860-9503
_v964
490 0 _aIntelligent Technologies and Robotics (SpringerNature-42732)
490 0 _aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505 0 _aIntroduction -- Neuro-Fuzzy Approach and its Application in Recommender Systems -- Novel Explainable Recommenders Based on Neuro-Fuzzy -- Explainable Recommender for Investment Advisers -- Summary and Final Remarks.
520 3 _aThe book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
988 _aSpringer_Robotics_2021
650 7 _2embne
_aInteligencia artificial
_9413115
776 0 8 _iPrinted edition:
_z9783030755201
776 0 8 _iPrinted edition:
_z9783030755225
776 0 8 _iPrinted edition:
_z9783030755232
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-75521-8
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE