LEADER 03222nam 22006135 450 001 9910492147403321 005 20251204104435.0 010 $a3-030-68817-8 024 7 $a10.1007/978-3-030-68817-2 035 $a(CKB)4100000011979260 035 $a(MiAaPQ)EBC6675991 035 $a(Au-PeEL)EBL6675991 035 $a(OCoLC)1260343779 035 $a(PPN)269144420 035 $a(BIP)80869778 035 $a(BIP)78761268 035 $a(DE-He213)978-3-030-68817-2 035 $a(EXLCZ)994100000011979260 100 $a20210710d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRepresentation Learning $ePropositionalization and Embeddings /$fby Nada Lavra?, Vid Podpe?an, Marko Robnik-?ikonja 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (175 pages) 311 08$a3-030-68816-X 327 $aIntroduction to Representation Learning -- Machine Learning Background -- Text Embeddings -- Propositionalization of Relational Data -- Graph and Heterogeneous Network Transformations -- Unified Representation Learning Approaches -- Many Faces of Representation Learning. 330 $aThis monograph addresses advances in representation learning, a cutting-edge research area of machine learning. Representation learning refers to modern data transformation techniques that convert data of different modalities and complexity, including texts, graphs, and relations, into compact tabular representations, which effectively capture their semantic properties and relations. The monograph focuses on (i) propositionalization approaches, established in relational learning and inductive logic programming, and (ii) embedding approaches, which have gained popularity with recent advances in deep learning. The authors establish a unifying perspective on representation learning techniques developed in these various areas of modern data science, enabling the reader to understand the common underlying principles and to gain insight using selected examples and sample Python code. The monograph should be of interest to a wide audience, ranging from data scientists, machine learning researchers and students to developers, software engineers and industrial researchers interested in hands-on AI solutions. 606 $aData mining 606 $aArtificial intelligence$xData processing 606 $aNumerical analysis 606 $aData Mining and Knowledge Discovery 606 $aData Science 606 $aNumerical Analysis 615 0$aData mining. 615 0$aArtificial intelligence$xData processing. 615 0$aNumerical analysis. 615 14$aData Mining and Knowledge Discovery. 615 24$aData Science. 615 24$aNumerical Analysis. 676 $a006.31 700 $aLavrac?$b Nada$0853929 702 $aPodpec?an$b Vid 702 $aRobnik-Sikonja$b Marko 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910492147403321 996 $aRepresentation Learning$92174998 997 $aUNINA