LEADER 05911nam 22006735 450 001 996466132703316 005 20200704230407.0 010 $a3-540-35296-1 024 7 $a10.1007/11776420 035 $a(CKB)1000000000283911 035 $a(SSID)ssj0000318645 035 $a(PQKBManifestationID)11249848 035 $a(PQKBTitleCode)TC0000318645 035 $a(PQKBWorkID)10310455 035 $a(PQKB)11065307 035 $a(DE-He213)978-3-540-35296-9 035 $a(MiAaPQ)EBC3068083 035 $a(PPN)123135974 035 $a(EXLCZ)991000000000283911 100 $a20100301d2006 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aLearning Theory$b[electronic resource] $e19th Annual Conference on Learning Theory, COLT 2006, Pittsburgh, PA, USA, June 22-25, 2006, Proceedings /$fedited by Hans Ulrich Simon, Gábor Lugosi 205 $a1st ed. 2006. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006. 215 $a1 online resource (XII, 660 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4005 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-35294-5 320 $aIncludes bibliographical references and index. 327 $aInvited Presentations -- Random Multivariate Search Trees -- On Learning and Logic -- Predictions as Statements and Decisions -- Clustering, Un-, and Semisupervised Learning -- A Sober Look at Clustering Stability -- PAC Learning Axis-Aligned Mixtures of Gaussians with No Separation Assumption -- Stable Transductive Learning -- Uniform Convergence of Adaptive Graph-Based Regularization -- Statistical Learning Theory -- The Rademacher Complexity of Linear Transformation Classes -- Function Classes That Approximate the Bayes Risk -- Functional Classification with Margin Conditions -- Significance and Recovery of Block Structures in Binary Matrices with Noise -- Regularized Learning and Kernel Methods -- Maximum Entropy Distribution Estimation with Generalized Regularization -- Unifying Divergence Minimization and Statistical Inference Via Convex Duality -- Mercer?s Theorem, Feature Maps, and Smoothing -- Learning Bounds for Support Vector Machines with Learned Kernels -- Query Learning and Teaching -- On Optimal Learning Algorithms for Multiplicity Automata -- Exact Learning Composed Classes with a Small Number of Mistakes -- DNF Are Teachable in the Average Case -- Teaching Randomized Learners -- Inductive Inference -- Memory-Limited U-Shaped Learning -- On Learning Languages from Positive Data and a Limited Number of Short Counterexamples -- Learning Rational Stochastic Languages -- Parent Assignment Is Hard for the MDL, AIC, and NML Costs -- Learning Algorithms and Limitations on Learning -- Uniform-Distribution Learnability of Noisy Linear Threshold Functions with Restricted Focus of Attention -- Discriminative Learning Can Succeed Where Generative Learning Fails -- Improved Lower Bounds for Learning Intersections of Halfspaces -- Efficient Learning Algorithms Yield Circuit Lower Bounds -- Online Aggregation -- Optimal Oracle Inequality for Aggregation of Classifiers Under Low Noise Condition -- Aggregation and Sparsity Via ?1 Penalized Least Squares -- A Randomized Online Learning Algorithm for Better Variance Control -- Online Prediction and Reinforcement Learning I -- Online Learning with Variable Stage Duration -- Online Learning Meets Optimization in the Dual -- Online Tracking of Linear Subspaces -- Online Multitask Learning -- Online Prediction and Reinforcement Learning II -- The Shortest Path Problem Under Partial Monitoring -- Tracking the Best Hyperplane with a Simple Budget Perceptron -- Logarithmic Regret Algorithms for Online Convex Optimization -- Online Variance Minimization -- Online Prediction and Reinforcement Learning III -- Online Learning with Constraints -- Continuous Experts and the Binning Algorithm -- Competing with Wild Prediction Rules -- Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path -- Other Approaches -- Ranking with a P-Norm Push -- Subset Ranking Using Regression -- Active Sampling for Multiple Output Identification -- Improving Random Projections Using Marginal Information -- Open Problems -- Efficient Algorithms for General Active Learning -- Can Entropic Regularization Be Replaced by Squared Euclidean Distance Plus Additional Linear Constraints. 410 0$aLecture Notes in Artificial Intelligence ;$v4005 606 $aArtificial intelligence 606 $aComputers 606 $aAlgorithms 606 $aMathematical logic 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 615 0$aArtificial intelligence. 615 0$aComputers. 615 0$aAlgorithms. 615 0$aMathematical logic. 615 14$aArtificial Intelligence. 615 24$aComputation by Abstract Devices. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aMathematical Logic and Formal Languages. 676 $a006.3/1 702 $aSimon$b Hans Ulrich$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLugosi$b Gábor$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aConference on Learning Theory 906 $aBOOK 912 $a996466132703316 996 $aLearning Theory$9772233 997 $aUNISA LEADER 04217nam 2200493 450 001 9910522991203321 005 20220906004033.0 010 $a1-4842-7818-6 024 7 $a10.1007/978-1-4842-7818-5 035 $a(MiAaPQ)EBC6838902 035 $a(Au-PeEL)EBL6838902 035 $a(CKB)20275210100041 035 $a(OCoLC)1291318609 035 $a(OCoLC-P)1291318609 035 $a(CaSebORM)9781484278185 035 $a(EXLCZ)9920275210100041 100 $a20220906d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBeginning iPhone development with SwiftUI $eexploring the iOS SDK /$fWally Wang 205 $aSixth edition. 210 1$aBerkeley, California :$cApress L. P.,$d[2022] 210 4$d©2022 215 $a1 online resource (468 pages) 300 $aIncludes index. 311 08$aPrint version: Wang, Wally Beginning IPhone Development with SwiftUI Berkeley, CA : Apress L. P.,c2022 9781484278178 327 $aChapter 1: Understanding iOS Programming -- Chapter 2: Designing User Interfaces with SwiftUI -- Chapter 3: Placing Views on the User Interface -- Chapter 4: Working with Text -- Chapter 5: Working with Images -- Chapter 6: Responding to the User with Buttons and Segmented Controls -- Chapter 7: Retrieving Text from Text Fields and Text Editors -- Chapter 8: Limiting Choices with Pickers -- Chapter 9: Limiting Choices with Toggles, Steppers, and Sliders -- Chapter 10: Providing Options with Links and Menus -- Chapter 11: Touch Gestures -- Chapter 12: Using Alerts, Action Sheets, and Contextual Menus -- Chapter 13: Displaying Lists -- Chapter 14: Using Forms and Group Boxes -- Chapter 15: Using Disclosure Groups, Scroll Views, and Outline Groups -- Chapter 16: Using the Navigation View -- Chapter 17: Using the Tab View -- Chapter 18: Using Grids -- Chapter 19: Using Animation -- Chapter 20: Using GeometryReader -- Appendix: An Introduction to Swift. 330 $aTame the power of Apple's new user interface toolkit, SwiftUI. Integrate all the interface elements iOS users have come to know and love, such as buttons, switches, pickers, toolbars, and sliders with less effort and more efficiency. You'll also learn about touch gestures, lists, and grids for displaying data on a user interface. And you'll even go beyond those simple controls to liven up any user interface with simple animation techniques. Spice your designs up with movement, scaling, and resizing, including spring and bounce effects! You'll start with basic designs and then explore more sophisticated ones. Assuming little or no working knowledge of the Swift programming language, and written in a friendly, easy-to-follow style, this book offers a comprehensive course in iPhone and iPad programming. The book starts with a gentle introduction to using Xcode and then guides you though the creation of your first simple application. You'll create user interfaces for that application using multiple screens in two different ways-using Navigation View and Tab Bars. Beginning iPhone Development with Swift UI covers the basic information you need to get up and running quickly to turn your great ideas into working iOS apps with stunningly interactive interfaces using SwiftUI. Once you're ready, move on to Pro iPhone Development with Swift UI to learn more of the unique aspects of iOS programming and the Swift language. What You Will Learn Discover the basics of designing a user interface using SwiftUI Build cool, crisp user interfaces that use animation Display data in lists and outlines Organize user interfaces in forms and groups Who This Book is For Aspiring iOS app developers new to the Apple Swift programming language and/or the iOS SDK. 606 $aOperating systems (Computers) 606 $aApple computer 615 0$aOperating systems (Computers) 615 0$aApple computer. 676 $a004.165 700 $aWang$b Wally$0555404 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910522991203321 996 $aBeginning IPhone Development with SwiftUI$92592636 997 $aUNINA