LEADER 06231nam 22008413u 450 001 9910456565703321 005 20210108155149.0 010 $a1-283-17774-9 010 $a9786613177742 010 $a1-118-14851-7 035 $a(CKB)2550000000041295 035 $a(EBL)818779 035 $a(OCoLC)747411979 035 $a(SSID)ssj0000534375 035 $a(PQKBManifestationID)11359982 035 $a(PQKBTitleCode)TC0000534375 035 $a(PQKBWorkID)10511039 035 $a(PQKB)11354549 035 $a(CaSebORM)9781118148501 035 $a(MiAaPQ)EBC818779 035 $a(EXLCZ)992550000000041295 100 $a20130418d2011|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBeginning Microsoft Visual Studio LightSwitch Development$b[electronic resource] 205 $a1st edition 210 $aNew York $cWiley$d2011 215 $a1 online resource (468 p.) 225 0 $aWrox programmer to programmer Beginning Visual studio LightSwitch development 300 $aDescription based upon print version of record. 311 $a1-118-02195-9 327 $aBeginning: Visual Studio® LightSwitch Development; Contents; Introduction; Part I: An Introduction to Visual Studio LightSwitch; Chapter 1: Prototyping and Rapid Application Development; Line-of-Business Software Development Challenges; LOB Software Development; Changing Project Environment; Creating a Requirements Specification; Feedback Frequency; Application Prototyping; Wireframe Models; Proof-of-Concept Models; Low-Fidelity Prototypes; High-Fidelity Prototypes; Rapid Application Development; RAD Tools; Visual Studio LightSwitch and RAD; Summary 327 $aChapter 2: Getting Started With Visual Studio LightswitchGetting to Know Visual Studio; A Short History of Visual Studio; Roots; Other Visual Languages and Tools; Integrating Languages into Visual Studio; Moving to .NET; Visual Studio Editions; Visual Studio 2010; Getting to Know Visual Studio LightSwitch; Business Code versus Plumbing Code; LOB Applications and LightSwitch; Installing Visual Studio LightSwitch; Types of Installation; Running LightSwitch for the First Time; Creating Your First LightSwitch Application; Creating a Project; Creating a Table 327 $aCreating a Screen to List Album InformationMaking Runtime Customizations; Summary; Chapter 3: Technologies Behind a Lightswitch Application; The Three-Tier Application Architecture Pattern; The Presentation Tier; The Logic Tier; The Data Tier; LightSwitch and the Three-Tier Architecture Pattern; The .NET Framework; The .NET Runtime; .NET Languages; .NET Framework and LightSwitch; Sources for More Information about the .NET Framework; Silverlight 4; A New UI Concept; Layout; Data Binding; Styles and Templates; Sources for More Information about Silverlight 4; SQL Server 2008 327 $aSQL Server FeaturesSQL Server 2008 Express; Sources for More Information about SQL Server; SharePoint 2010; SharePoint 2010 Features; SharePoint 2010 Lists and LightSwitch; Microsoft Office; Microsoft Office Application Features; Exporting Information to Microsoft Excel; Windows Azure; Dynamic Resource Allocation; Application Development with Windows Azure; Windows Azure and LightSwitch; Sources for More Information about Windows Azure; Summary; Chapter 4: Customizing Lightswitch Applications; Customizing Data; Creating the Aquarium Database; Connecting to an Existing Database 327 $aChanging Names and Types in the Existing TableCreating the Cart Table; Customizing Screens; Creating a Creature Catalog; Changing the Grid Layout; Creating a Shopping Cart Screen; Writing Code; Setting a Default Property Value in Code; Setting the Startup Screen; Managing Price Information; Other Points Where Code Is Used; Summary; Part II: Creating Applications with Visual Studio LightSwitch; Chapter 5: Preparing to Develop a Lightswitch Application; The ProjectTrek Sample; The Functionality of ProjectTrek; The ProjectTrek Application Database; LightSwitch Application Development Life cycle 327 $aThe Iterative Model 330 $aLearn how LightSwitch can accelerate and simplify application development As Microsoft's newest offering for simplifying application development, LightSwitch opens the development door to creating applications without writing code. This introductory, full-color book shows you how to quickly create, modify, and distribute information for your business with LightSwitch. Packed with simple example programs, this beginner-level resource guides you through a complete small business application using LightSwitch to demonstrate the capabilities of this exciting new tool. You'll explore the 606 $aApplication software - Development - Computer programs 606 $aC# (Computer program language) 606 $aMicrosoft Visual C# 606 $aVisual programming (Computer science) - Computer programs 606 $aVisual programming (Computer science)$xComputer programs 606 $aApplication software$xDevelopment$xComputer programs 606 $aElectrical & Computer Engineering$2HILCC 606 $aEngineering & Applied Sciences$2HILCC 606 $aTelecommunications$2HILCC 606 $aComputer Science$2HILCC 608 $aElectronic books. 615 4$aApplication software - Development - Computer programs. 615 4$aC# (Computer program language). 615 4$aMicrosoft Visual C#. 615 4$aVisual programming (Computer science) - Computer programs. 615 0$aVisual programming (Computer science)$xComputer programs 615 0$aApplication software$xDevelopment$xComputer programs 615 7$aElectrical & Computer Engineering 615 7$aEngineering & Applied Sciences 615 7$aTelecommunications 615 7$aComputer Science 676 $a005.1 676 $a005.13/3 700 $aNov?k$b Istv?n$0903571 702 $aNovāak$b Istvāan 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910456565703321 996 $aBeginning Microsoft Visual Studio LightSwitch Development$92019870 997 $aUNINA LEADER 10894nam 2200493 450 001 9910677590703321 005 20230124180940.0 010 $a1-119-60097-9 010 $a1-119-60098-7 035 $a(MiAaPQ)EBC6943531 035 $a(Au-PeEL)EBL6943531 035 $a(CKB)21448663100041 035 $a(EXLCZ)9921448663100041 100 $a20221112d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultiblock data fusion in statistics and machine learning $eapplications in the natural and life sciences /$fAge K. Smilde, Tormod Nęs, Kristian Hovde Liland 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2022] 210 4$d©2022 215 $a1 online resource (418 pages) 311 08$aPrint version: Smilde, Age K. Multiblock Data Fusion in Statistics and Machine Learning Newark : John Wiley & Sons, Incorporated,c2022 9781119600961 320 $aIncludes bibliographical references and index. 327 $aIntro -- Multiblock Data Fusion in Statistics and Machine Learning -- Contents -- Foreword -- Preface -- List of Figures -- List of Tables -- Part I Introductory Concepts and Theory -- chapnumcolor1 Introduction -- 1.1 Scope of the Book -- 1.2 Potential Audience -- 1.3 Types of Data and Analyses -- 1.3.1 Supervised and Unsupervised Analyses -- 1.3.2 High-, Mid- and Low-level Fusion -- 1.3.3 Dimension Reduction -- 1.3.4 Indirect Versus Direct Data -- 1.3.5 Heterogeneous Fusion -- 1.4 Examples -- 1.4.1 Metabolomics -- 1.4.2 Genomics -- 1.4.3 Systems Biology -- 1.4.4 Chemistry -- 1.4.5 Sensory Science -- 1.5 Goals of Analyses -- 1.6 Some History -- 1.7 Fundamental Choices -- 1.8 Common and Distinct Components -- 1.9 Overview and Links -- 1.10 Notation and Terminology -- 1.11 Abbreviations -- chapnumcolor2 Basic Theory and Concepts -- 2.i General Introduction -- 2.1 Component Models -- 2.1.1 General Idea of Component Models -- 2.1.2 Principal Component Analysis -- 2.1.3 Sparse PCA -- 2.1.4 Principal Component Regression -- 2.1.5 Partial Least Squares -- 2.1.6 Sparse PLS -- 2.1.7 Principal Covariates Regression -- 2.1.8 Redundancy Analysis -- 2.1.9 Comparing PLS, PCovR and RDA -- 2.1.10 Generalised Canonical Correlation Analysis -- 2.1.11 Simultaneous Component Analysis -- 2.2 Properties of Data -- 2.2.1 Data Theory -- 2.2.2 Scale-types -- 2.3 Estimation Methods -- 2.3.1 Least-squares Estimation -- 2.3.2 Maximum-likelihood Estimation -- 2.3.3 Eigenvalue Decomposition-based Methods -- 2.3.4 Covariance or Correlation-based Estimation Methods -- 2.3.5 Sequential Versus Simultaneous Methods -- 2.3.6 Homogeneous Versus Heterogeneous Fusion -- 2.4 Within- and Between-block Variation -- 2.4.1 Definition and Example -- 2.4.2 MAXBET Solution -- 2.4.3 MAXNEAR Solution -- 2.4.4 PLS2 Solution -- 2.4.5 CCA Solution -- 2.4.6 Comparing the Solutions. 327 $a2.4.7 PLS, RDA and CCA Revisited -- 2.5 Framework for Common and Distinct Components -- 2.6 Preprocessing -- 2.7 Validation -- 2.7.1 Outliers -- 2.7.1.1 Residuals -- 2.7.1.2 Leverage -- 2.7.2 Model Fit -- 2.7.3 Bias-variance Trade-off -- 2.7.4 Test Set Validation -- 2.7.5 Cross-validation -- 2.7.6 Permutation Testing -- 2.7.7 Jackknife and Bootstrap -- 2.7.8 Hyper-parameters and Penalties -- 2.8 Appendix -- chapnumcolor3 Structure of Multiblock Data -- 3.i General Introduction -- 3.1 Taxonomy -- 3.2 Skeleton of a Multiblock Data Set -- 3.2.1 Shared Sample Mode -- 3.2.2 Shared Variable Mode -- 3.2.3 Shared Variable or Sample Mode -- 3.2.4 Shared Variable and Sample Mode -- 3.3 Topology of a Multiblock Data Set -- 3.3.1 Unsupervised Analysis -- 3.3.2 Supervised Analysis -- 3.4 Linking Structures -- 3.4.1 Linking Structure for Unsupervised Analysis -- 3.4.2 Linking Structures for Supervised Analysis -- 3.5 Summary -- chapnumcolor4 Matrix Correlations -- 4.i General Introduction -- 4.1 Definition -- 4.2 Most Used Matrix Correlations -- 4.2.1 Inner Product Correlation -- 4.2.2 GCD coefficient -- 4.2.3 RV-coefficient -- 4.2.4 SMI-coefficient -- 4.3 Generic Framework of Matrix Correlations -- 4.4 Generalised Matrix Correlations -- 4.4.1 Generalised RV-coefficient -- 4.4.2 Generalised Association Coefficient -- 4.5 Partial Matrix Correlations -- 4.6 Conclusions and Recommendations -- 4.7 Open Issues -- Part II Selected Methods for Unsupervised and Supervised Topologies -- chapnumcolor5 Unsupervised Methods -- 5.i General Introduction -- 5.ii Relations to the General Framework -- 5.1 Shared Variable Mode -- 5.1.1 Only Common Variation -- 5.1.1.1 Simultaneous Component Analysis -- 5.1.1.2 Clustering and SCA -- 5.1.1.3 Multigroup Data Analysis -- 5.1.2 Common, Local, and Distinct Variation -- 5.1.2.1 Distinct and Common Components. 327 $a5.1.2.2 Multivariate Curve Resolution -- 5.2 Shared Sample Mode -- 5.2.1 Only Common Variation -- 5.2.1.1 SUM-PCA -- 5.2.1.2 Multiple Factor Analysis and STATIS -- 5.2.1.3 Generalised Canonical Analysis -- 5.2.1.4 Regularised Generalised Canonical Correlation Analysis -- 5.2.1.5 Exponential Family SCA -- 5.2.1.6 Optimal-scaling -- 5.2.2 Common, Local, and Distinct Variation -- 5.2.2.1 Joint and Individual Variation Explained -- 5.2.2.2 Distinct and Common Components -- 5.2.2.3 PCA-GCA -- 5.2.2.4 Advanced Coupled Matrix and Tensor Factorisation -- 5.2.2.5 Penalised-ESCA -- 5.2.2.6 Multivariate Curve Resolution -- 5.3 Generic Framework -- 5.3.1 Framework for Simultaneous Unsupervised Methods -- 5.3.1.1 Description of the Framework -- 5.3.1.2 Framework Applied to Simultaneous Unsupervised Data Analysis Methods -- 5.3.1.3 Framework of Common/Distinct Applied to Simultaneous Unsupervised Multiblock Data Analysis Methods -- 5.4 Conclusions and Recommendations -- 5.5 Open Issues -- chapnumcolor6 ASCA and Extensions -- 6.i General Introduction -- 6.ii Relations to the General Framework -- 6.1 ANOVA-Simultaneous Component Analysis -- 6.1.1 The ASCA Method -- 6.1.2 Validation of ASCA -- 6.1.2.1 Permutation Testing -- 6.1.2.2 Back-projection -- 6.1.2.3 Confidence Ellipsoids -- 6.1.3 The ASCA+ and LiMM-PCA Methods -- 6.2 Multilevel-SCA -- 6.3 Penalised-ASCA -- 6.4 Conclusions and Recommendations -- 6.5 Open Issues -- chapnumcolor7 Supervised Methods -- 7.i General Introduction -- 7.ii Relations to the General Framework -- 7.1 Multiblock Regression: General Perspectives -- 7.1.1 Model and Assumptions -- 7.1.2 Different Challenges and Aims -- 7.2 Multiblock PLS Regression -- 7.2.1 Standard Multiblock PLS Regression -- 7.2.2 MB-PLS Used for Classification -- 7.2.3 Sparse Multiblock PLS Regression (sMB-PLS). 327 $a7.3 The Family of SO-PLS Regression Methods (Sequential and Orthogonalised PLS Regression) -- 7.3.1 The SO-PLS Method -- 7.3.2 Order of Blocks -- 7.3.3 Interpretation Tools -- 7.3.4 Restricted PLS Components and their Application in SO-PLS -- 7.3.5 Validation and Component Selection -- 7.3.6 Relations to ANOVA -- 7.3.7 Extensions of SO-PLS to Handle Interactions Between Blocks -- 7.3.8 Further Applications of SO-PLS -- 7.3.9 Relations Between SO-PLS and ASCA -- 7.4 Parallel and Orthogonalised PLS (PO-PLS) Regression -- 7.5 Response Oriented Sequential Alternation -- 7.5.1 The ROSA Method -- 7.5.2 Validation -- 7.5.3 Interpretation -- 7.6 Conclusions and Recommendations -- 7.7 Open Issues -- Part III Methods for Complex Multiblock Structures -- chapnumcolor8 Complex Block Structures -- with Focus on L-Shape Relations -- 8.i General Introduction -- 8.ii Relations to the General Framework -- 8.1 Analysis of L-shape Data: General Perspectives -- 8.2 Sequential Procedures for L-shape Data Based on PLS/PCR and ANOVA -- 8.2.1 Interpretation of X1, Quantitative X2-data, Horizontal Axis First -- 8.2.2 Interpretation of X1, Categorical X2-data, Horizontal Axis First -- 8.2.3 Analysis of Segments/Clusters of X1 Data -- 8.3 The L-PLS Method for Joint Estimation of Blocks in L-shape Data -- 8.3.1 The Original L-PLS Method, Endo-L-PLS -- 8.3.2 Exo- Versus Endo-L-PLS -- 8.4 Modifications of the Original L-PLS Idea -- 8.4.1 Weighting Information from X3 and X1 in L-PLS Using a Parameter "? -- 8.4.2 Three-blocks Bifocal PLS -- 8.5 Alternative L-shape Data Analysis Methods -- 8.5.1 Principal Component Analysis with External Information -- 8.5.2 A Simple PCA Based Procedure for Using Unlabelled Data in Calibration -- 8.5.3 Multivariate Curve Resolution for Incomplete Data -- 8.5.4 An Alternative Approach in Consumer Science Based on Correlations Between X3 and X1. 327 $a8.6 Domino PLS and More Complex Data Structures -- 8.7 Conclusions and Recommendations -- 8.8 Open Issues -- Part IV Alternative Methods for Unsupervised and Supervised Topologies -- chapnumcolor9 Alternative Unsupervised Methods -- 9.i General Introduction -- 9.ii Relationship to the General Framework -- 9.1 Shared Variable Mode -- 9.2 Shared Sample Mode -- 9.2.1 Only Common Variation -- 9.2.1.1 DIABLO -- 9.2.1.2 Generalised Coupled Tensor Factorisation -- 9.2.1.3 Representation Matrices -- 9.2.1.4 Extended PCA -- 9.2.2 Common, Local, and Distinct Variation -- 9.2.2.1 Generalised SVD -- 9.2.2.2 Structural Learning and Integrative Decomposition -- 9.2.2.3 Bayesian Inter-battery Factor Analysis -- 9.2.2.4 Group Factor Analysis -- 9.2.2.5 OnPLS -- 9.2.2.6 Generalised Association Study -- 9.2.2.7 Multi-Omics Factor Analysis -- 9.3 Two Shared Modes and Only Common Variation -- 9.3.1 Generalised Procrustes Analysis -- 9.3.2 Three-way Methods -- 9.4 Conclusions and Recommendations -- 9.4.1 Open Issues -- chapnumcolor10 Alternative Supervised Methods -- 10.i General Introduction -- 10.ii Relations to the General Framework -- 10.1 Model and Focus -- 10.2 Extension of PCovR -- 10.2.1 Sparse Multiblock Principal Covariates Regression, Sparse PCovR -- 10.2.2 Multiway Multiblock Covariates Regression -- 10.3 Multiblock Redundancy Analysis -- 10.3.1 Standard Multiblock Redundancy Analysis -- 10.3.2 Sparse Multiblock Redundancy Analysis -- 10.4 Miscellaneous Multiblock Regression Methods -- 10.4.1 Multiblock Variance Partitioning -- 10.4.2 Network Induced Supervised Learning -- 10.4.3 Common Dimensions for Multiblock Regression -- 10.5 Modifications and Extensions of the SO-PLS Method -- 10.5.1 Extensions of SO-PLS to Three-Way Data -- 10.5.2 Variable Selection for SO-PLS -- 10.5.3 More Complicated Error Structure for SO-PLS. 327 $a10.5.4 SO-PLS Used for Path Modelling. 606 $aScience$xStatistical methods 615 0$aScience$xStatistical methods. 676 $a519.52 700 $aSmilde$b Age K.$0855361 702 $aLiland$b Kristian H$g(Kristian Hovde), 702 $aNęs$b Tormod 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910677590703321 996 $aMultiblock data fusion in statistics and machine learning$93069491 997 $aUNINA