LEADER 01064nam0-22003251i-450- 001 990007401640403321 005 20050223152146.0 035 $a000740164 035 $aFED01000740164 035 $a(Aleph)000740164FED01 035 $a000740164 100 $a20030326d1976----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aClasse operaia e ceti medi$e[la strategia delle alleanze nel dibattito socialista degli anni Trenta]$fSimona Colarizi$gintroduzione di Claudio Signorile$gcon interventi di F. Alberoni, L. Cafagna e F. Momigliano 210 $aVenezia$cMarsilio$d1976 215 $aX, 171 p.$d24 cm 225 1 $aSocialismo oggi$v6 700 1$aColarizi,$bSimona$0133907 702 1$aMomigliano,$bFranco 702 1$aAlberoni,$bFrancesco$f<1929- > 702 1$aCafagna,$bLuciano$f<1926- > 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007401640403321 952 $aCOLLEZ. 413 (91)$fFSPBC 959 $aFSPBC 996 $aClasse operaia e ceti medi$9277235 997 $aUNINA LEADER 06128nam 2200481 450 001 9910492147403321 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$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910492147403321 996 $aRepresentation Learning$92174998 997 $aUNINA LEADER 02975nam 2200349z 450 001 9910793474203321 005 20230511171416.0 010 $a0-522-86866-5 035 $a(CKB)4100000007758664 035 $a(MiAaPQ)EBC5676174 035 $a(EXLCZ)994100000007758664 100 $a20190317d2016 uy 0 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 04$aThe AIF in battle $ehow the Australian Imperial Force fought, 1914-1918 /$feditor, Jean Bou 210 1$aCarlton, Vic. :$cMUP Academic Digital,$d2016. 215 $a1 online resource 300 $aIncludes index. 311 0 $a0-522-86865-7 327 $aIntro; Title; Copyright; Contents; List of Contributors; Editor's note and Acknowledgements; List of Abbreviations; Maps; Introduction; 1 Cavalry Combat: Mounted Warfare in Palestine; 2 The Battalion: The AIF Infantry Battalion and its Evolution; 3 Indirect Fire: The AIF's Artillery and Mortars on the Western Front; 4 The AIF's Commanders: Learning on the Job; 5 Over the Western Front: Air Power and the AIF; 6 Below Ground: The AIF's Mining Operations; 7 'Nightly Suicide Operations': Trench Raids and the Development of the AIF 8 From the Somme to the Salient: The AIF and its Battles, 1916-19179 'Backs to the Wall': Australians on the Western Front, January-June 1918; 10 The AIF and the Hundred Days: 'Orchestration' for Tactical Success in 1918; Appendix A: Infantry Battalion Organisation Diagrams; Appendix B: Artillery Organisation Diagrams; Index 330 $aBy the end of the First World War the combat formations of the Australian Imperial Force (AIF) in both France and the Middle East were considered among the British Empire's most effective troops. While sometimes a source of pride and not a little boasting, how the force came to be so was not due to any inherent national prowess or trait. Instead it was the culmination of years of training, organisational change, battlefield experimentation and hard-won experience;a process that included not just the Australians, but the wider British imperial armies as well. This book brings together some of Australia's foremost military historians to outline how the military neophytes that left Australia's shores in 1914 became the battle winning troops of 1918. It will trace the evolution of several of the key arms of the AIF, including the infantry, the light horse, the artillery, and the flying corps, and also consider how the various arms worked together alongside other troops of the British Empire to achieve a remarkably high level of battlefield effectiveness. 606 $aWorld War, 1914-1918$xParticipation, Australian 607 $aAustralia$xHistory, Military$y1914-1918 615 0$aWorld War, 1914-1918$xParticipation, Australian. 676 $a940.40994 702 $aBou$b Jean 906 $aBOOK 912 $a9910793474203321 996 $aThe AIF in battle$93857289 997 $aUNINA