LEADER 03267nam 22006015 450 001 9910483445703321 005 20200702225146.0 010 $a3-030-43883-X 024 7 $a10.1007/978-3-030-43883-8 035 $a(CKB)4100000011223360 035 $a(MiAaPQ)EBC6298759 035 $a(DE-He213)978-3-030-43883-8 035 $a(PPN)243761295 035 $a(EXLCZ)994100000011223360 100 $a20200403d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Trends in Learning From Data $eTutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) /$fedited by Luca Oneto, Nicoḷ Navarin, Alessandro Sperduti, Davide Anguita 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (vii, 221 pages) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v896 311 $a3-030-43882-1 327 $aIntroduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences. 330 $aThis book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v896 606 $aComputational intelligence 606 $aMachine learning 606 $aEngineering?Data processing 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aData Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11040 615 0$aComputational intelligence. 615 0$aMachine learning. 615 0$aEngineering?Data processing. 615 14$aComputational Intelligence. 615 24$aMachine Learning. 615 24$aData Engineering. 676 $a006.31 702 $aOneto$b Luca$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNavarin$b Nicoḷ$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSperduti$b Alessandro$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAnguita$b Davide$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483445703321 996 $aRecent Trends in Learning From Data$92853507 997 $aUNINA