LEADER 01522nam 2200385 n 450 001 996391745303316 005 20200824120959.0 035 $a(CKB)1000000000670568 035 $a(EEBO)2240858477 035 $a(UnM)99841071e 035 $a(UnM)99841071 035 $a(EXLCZ)991000000000670568 100 $a19910315d1576 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 02$aA treatise of the immortalitie of the soule$b[electronic resource] $ewherein is declared the origine, nature, and powers of the same, together with the state and condition thereof, both as it is conioyned and dissolued from the body. Made by Iohn Woolton minister of the Gospell 210 $aImprinted at London $cIn Paules Churchyarde, at the signe of the Brasen Serpent, by [Thomas Purfoote for] Iohn Shepperd$dAnno Dom. 1576 215 $a[10], 96 leaves $cill. (woodcut) 300 $aActual printer's name from colophon. 300 $aRunning title reads: Of the immortalitie of the soule. 300 $aReproduction of the original in the University of Chicago. Library. 330 $aeebo-0165 606 $aSoul$vEarly works to 1800 606 $aImmortality$vEarly works to 1800 615 0$aSoul 615 0$aImmortality 700 $aWoolton$b John$f1535?-1594.$01003879 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996391745303316 996 $aA treatise of the immortalitie of the soule$92337889 997 $aUNISA LEADER 06126nam 2200481 450 001 996466391103316 005 20220530142830.0 010 $a3-030-68817-8 035 $a(CKB)4100000011979260 035 $a(MiAaPQ)EBC6675991 035 $a(Au-PeEL)EBL6675991 035 $a(OCoLC)1260343779 035 $a(PPN)269144420 035 $a(EXLCZ)994100000011979260 100 $a20220326d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRepresentation learning $epropositionalization and embeddings /$fNada Lavrac, Vid Podpecan, Marko Robnik-Sikonja 210 1$aCham, Switzerland :$cSpringer,$d2021. 215 $a1 online resource (175 pages) 311 $a3-030-68816-X 327 $aIntro -- Foreword -- Preface -- Contents -- 1 Introduction to Representation Learning -- 1.1 Motivation -- 1.2 Representation Learning in Knowledge Discovery -- 1.2.1 Machine Learning and Knowledge Discovery -- 1.2.2 Automated Data Transformation -- 1.3 Data Transformations and Information Representation Levels -- 1.3.1 Information Representation Levels -- 1.3.2 Propositionalization: Learning Symbolic Vector Representations -- 1.3.3 Embeddings: Learning Numeric Vector Representations -- 1.4 Evaluation of Propositionalization and Embeddings -- 1.4.1 Performance Evaluation -- 1.4.2 Interpretability -- 1.5 Survey of Automated Data Transformation Methods -- 1.6 Outline of This Monograph -- References -- 2 Machine Learning Background -- 2.1 Machine Learning -- 2.1.1 Attributes and Features -- 2.1.2 Machine Learning Approaches -- 2.1.3 Decision and Regression Tree Learning -- 2.1.4 Rule Learning -- 2.1.5 Kernel Methods -- 2.1.6 Ensemble Methods -- 2.1.7 Deep Neural Networks -- 2.2 Text Mining -- 2.3 Relational Learning -- 2.4 Network Analysis -- 2.4.1 Selected Homogeneous Network Analysis Tasks -- 2.4.2 Selected Heterogeneous Network Analysis Tasks -- 2.4.3 Semantic Data Mining -- 2.4.4 Network Representation Learning -- 2.5 Evaluation -- 2.5.1 Classifier Evaluation Measures -- 2.5.2 Rule Evaluation Measures -- 2.6 Data Mining and Selected Data Mining Platforms -- 2.6.1 Data Mining -- 2.6.2 Selected Data Mining Platforms -- 2.7 Implementation and Reuse -- References -- 3 Text Embeddings -- 3.1 Background Technologies -- 3.1.1 Transfer Learning -- 3.1.2 Language Models -- 3.2 Word Cooccurrence-Based Embeddings -- 3.2.1 Sparse Word Cooccurrence-Based Embeddings -- 3.2.2 Weighting Schemes -- 3.2.3 Similarity Measures -- 3.2.4 Sparse Matrix Representations of Texts -- 3.2.5 Dense Term-Matrix Based Word Embeddings -- 3.2.6 Dense Topic-Based Embeddings. 327 $a3.3 Neural Word Embeddings -- 3.3.1 Word2vec Embeddings -- 3.3.2 GloVe Embeddings -- 3.3.3 Contextual Word Embeddings -- 3.4 Sentence and Document Embeddings -- 3.5 Cross-Lingual Embeddings -- 3.6 Intrinsic Evaluation of Text Embeddings -- 3.7 Implementation and Reuse -- 3.7.1 LSA and LDA -- 3.7.2 word2vec -- 3.7.3 BERT -- References -- 4 Propositionalization of Relational Data -- 4.1 Relational Learning -- 4.2 Relational Data Representation -- 4.2.1 Illustrative Example -- 4.2.2 Example Using a Logical Representation -- 4.2.3 Example Using a Relational Database Representation -- 4.3 Propositionalization -- 4.3.1 Relational Features -- 4.3.2 Automated Construction of Relational Features by RSD -- 4.3.3 Automated Data Transformation and Learning -- 4.4 Selected Propositionalization Approaches -- 4.5 Wordification: Unfolding Relational Data into BoW Vectors -- 4.5.1 Outline of the Wordification Approach -- 4.5.2 Wordification Algorithm -- 4.5.3 Improved Efficiency of Wordification Algorithm -- 4.6 Deep Relational Machines -- 4.7 Implementation and Reuse -- 4.7.1 Wordification -- 4.7.2 Python-rdm Package -- References -- 5 Graph and Heterogeneous Network Transformations -- 5.1 Embedding Simple Graphs -- 5.1.1 DeepWalk Algorithm -- 5.1.2 Node2vec Algorithm -- 5.1.3 Other Random Walk-Based Graph Embedding Algorithms -- 5.2 Embedding Heterogeneous Information Networks -- 5.2.1 Heterogeneous Information Networks -- 5.2.2 Examples of Heterogeneous Information Networks -- 5.2.3 Embedding Feature-Rich Graphs with GCNs -- 5.2.4 Other Heterogeneous Network Embedding Approaches -- 5.3 Propositionalizing Heterogeneous Information Networks -- 5.3.1 TEHmINe Propositionalization of Text-Enriched Networks -- 5.3.1.1 Heterogeneous Network Decomposition -- 5.3.1.2 Feature Vector Construction -- 5.3.1.3 Data Fusion -- 5.3.2 HINMINE Heterogeneous Networks Decomposition. 327 $a5.4 Ontology Transformations -- 5.4.1 Ontologies and Semantic Data Mining -- 5.4.2 NetSDM Ontology Reduction Methodology -- 5.4.2.1 Converting Ontology and Examples into Network Format -- 5.4.2.2 Term Significance Calculation -- 5.4.2.3 Network Node Removal -- 5.5 Embedding Knowledge Graphs -- 5.6 Implementation and Reuse -- 5.6.1 Node2vec -- 5.6.2 Metapath2vec -- 5.6.3 HINMINE -- References -- 6 Unified Representation Learning Approaches -- 6.1 Entity Embeddings with StarSpace -- 6.2 Unified Approaches for Relational Data -- 6.2.1 PropStar: Feature-Based Relational Embeddings -- 6.2.2 PropDRM: Instance-Based Relational Embeddings -- 6.2.3 Performance Evaluation of Relational Embeddings -- 6.3 Implementation and Reuse -- 6.3.1 StarSpace -- 6.3.2 PropDRM -- References -- 7 Many Faces of Representation Learning -- 7.1 Unifying Aspects in Terms of Data Representation -- 7.2 Unifying Aspects in Terms of Learning -- 7.3 Unifying Aspects in Terms of Use -- 7.4 Summary and Conclusions -- References -- Index. 606 $aMachine learning 606 $aAprenentatge automātic$2thub 608 $aLlibres electrōnics$2thub 615 0$aMachine learning. 615 7$aAprenentatge automātic 676 $a006.31 700 $aLavrac?$b Nada$0853929 702 $aPodpec?an$b Vid 702 $aRobnik-Sikonja$b Marko 801 0$bMiAaPQ 801 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