001437289 000__ 03432cam\a2200529\i\4500
001437289 001__ 1437289
001437289 003__ OCoLC
001437289 005__ 20230309004141.0
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001437289 007__ cr\cn\nnnunnun
001437289 008__ 210612s2021\\\\sz\a\\\\ob\\\\000\0\eng\d
001437289 019__ $$a1256251346$$a1256265700$$a1284942354
001437289 020__ $$a9783030755218$$q(electronic bk.)
001437289 020__ $$a3030755215$$q(electronic bk.)
001437289 020__ $$z9783030755201
001437289 020__ $$z3030755207
001437289 0247_ $$a10.1007/978-3-030-75521-8$$2doi
001437289 035__ $$aSP(OCoLC)1256238106
001437289 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001437289 049__ $$aISEA
001437289 050_4 $$aHG4515.5$$b.R88 2021
001437289 08204 $$a332.640285$$223
001437289 1001_ $$aRutkowski, Tom,$$eauthor.
001437289 24510 $$aExplainable artificial intelligence based on neuro-fuzzy modeling with applications in finance /$$cTom Rutkowski.
001437289 264_1 $$aCham :$$bSpringer,$$c[2021]
001437289 264_4 $$c©2021
001437289 300__ $$a1 online resource (175 pages) :$$billustrations (some color)
001437289 336__ $$atext$$btxt$$2rdacontent
001437289 337__ $$acomputer$$bc$$2rdamedia
001437289 338__ $$aonline resource$$bcr$$2rdacarrier
001437289 4901_ $$aStudies in computational intelligence ;$$vvolume 964
001437289 504__ $$aIncludes bibliographical references.
001437289 5050_ $$aIntroduction -- Neuro-Fuzzy Approach and its Application in Recommender Systems -- Novel Explainable Recommenders Based on Neuro-Fuzzy -- Explainable Recommender for Investment Advisers -- Summary and Final Remarks.
001437289 506__ $$aAccess limited to authorized users.
001437289 520__ $$aThe book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
001437289 588__ $$aDescription based on print version record.
001437289 650_0 $$aArtificial intelligence$$xFinancial applications.
001437289 650_6 $$aIntelligence artificielle$$xApplications financières.
001437289 655_0 $$aElectronic books.
001437289 77608 $$iPrint version:$$aRutkowski, Tom.$$tExplainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance.$$dCham : Springer International Publishing AG, ©2021$$z9783030755201
001437289 830_0 $$aStudies in computational intelligence ;$$vv. 964.
001437289 852__ $$bebk
001437289 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-75521-8$$zOnline Access$$91397441.1
001437289 909CO $$ooai:library.usi.edu:1437289$$pGLOBAL_SET
001437289 980__ $$aBIB
001437289 980__ $$aEBOOK
001437289 982__ $$aEbook
001437289 983__ $$aOnline
001437289 994__ $$a92$$bISE