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MARC Lista (tabellare)
Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (216 pages)
Disciplina 515.9
Soggetto topico Artificial intelligence
Artificial intelligence - Data processing
ISBN 9789811904011
9789811904004
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Overcome Your Kernel Weakness -- What Makes KMMP Unique? -- Acknowledgments -- Contents -- 1 Positive Definite Kernels -- 1.1 Positive Definiteness of a Matrix -- 1.2 Kernels -- 1.3 Positive Definite Kernels -- 1.4 Probability -- 1.5 Bochner's Theorem -- 1.6 Kernels for Strings, Trees, and Graphs -- Appendix -- Exercises 1 sim 15 -- 2 Hilbert Spaces -- 2.1 Metric Spaces and Their Completeness -- 2.2 Linear Spaces and Inner Product Spaces -- 2.3 Hilbert Spaces -- 2.4 Projection Theorem -- 2.5 Linear Operators -- 2.6 Compact Operators -- Appendix: Proofs of Propositions -- Exercises 16 sim 30 -- 3 Reproducing Kernel Hilbert Space -- 3.1 RKHSs -- 3.2 Sobolev Space -- 3.3 Mercer's Theorem -- Appendix -- Exercises 31 sim 45 -- 4 Kernel Computations -- 4.1 Kernel Ridge Regression -- 4.2 Kernel Principle Component Analysis -- 4.3 Kernel SVM -- 4.4 Spline Curves -- 4.5 Random Fourier Features -- 4.6 Nyström Approximation -- 4.7 Incomplete Cholesky Decomposition -- Appendix -- Exercises 46 sim 64 -- 5 The MMD and HSIC -- 5.1 Random Variables in RKHSs -- 5.2 The MMD and Two-Sample Problem -- 5.3 The HSIC and Independence Test -- 5.4 Characteristic and Universal Kernels -- 5.5 Introduction to Empirical Processes -- Appendix -- Exercises 65 sim83 -- 6 Gaussian Processes and Functional Data Analyses -- 6.1 Regression -- 6.2 Classification -- 6.3 Gaussian Processes with Inducing Variables -- 6.4 Karhunen-Lóeve Expansion -- 6.5 Functional Data Analysis -- Appendix -- Exercises 83sim100 -- Appendix Bibliography.
Record Nr. UNINA-9910568287403321
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (216 pages)
Disciplina 515.9
Soggetto topico Artificial intelligence
Artificial intelligence - Data processing
ISBN 9789811904011
9789811904004
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Overcome Your Kernel Weakness -- What Makes KMMP Unique? -- Acknowledgments -- Contents -- 1 Positive Definite Kernels -- 1.1 Positive Definiteness of a Matrix -- 1.2 Kernels -- 1.3 Positive Definite Kernels -- 1.4 Probability -- 1.5 Bochner's Theorem -- 1.6 Kernels for Strings, Trees, and Graphs -- Appendix -- Exercises 1 sim 15 -- 2 Hilbert Spaces -- 2.1 Metric Spaces and Their Completeness -- 2.2 Linear Spaces and Inner Product Spaces -- 2.3 Hilbert Spaces -- 2.4 Projection Theorem -- 2.5 Linear Operators -- 2.6 Compact Operators -- Appendix: Proofs of Propositions -- Exercises 16 sim 30 -- 3 Reproducing Kernel Hilbert Space -- 3.1 RKHSs -- 3.2 Sobolev Space -- 3.3 Mercer's Theorem -- Appendix -- Exercises 31 sim 45 -- 4 Kernel Computations -- 4.1 Kernel Ridge Regression -- 4.2 Kernel Principle Component Analysis -- 4.3 Kernel SVM -- 4.4 Spline Curves -- 4.5 Random Fourier Features -- 4.6 Nyström Approximation -- 4.7 Incomplete Cholesky Decomposition -- Appendix -- Exercises 46 sim 64 -- 5 The MMD and HSIC -- 5.1 Random Variables in RKHSs -- 5.2 The MMD and Two-Sample Problem -- 5.3 The HSIC and Independence Test -- 5.4 Characteristic and Universal Kernels -- 5.5 Introduction to Empirical Processes -- Appendix -- Exercises 65 sim83 -- 6 Gaussian Processes and Functional Data Analyses -- 6.1 Regression -- 6.2 Classification -- 6.3 Gaussian Processes with Inducing Variables -- 6.4 Karhunen-Lóeve Expansion -- 6.5 Functional Data Analysis -- Appendix -- Exercises 83sim100 -- Appendix Bibliography.
Record Nr. UNISA-996475762603316
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Kernel Methods for Machine Learning with Math and RaKernel methods for machine learning with math and R : 100 exercises for building logic / / Joe Suzuki
Kernel Methods for Machine Learning with Math and RaKernel methods for machine learning with math and R : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (203 pages)
Disciplina 006.31
Soggetto topico R (Computer program language)
Kernel functions
ISBN 9789811903984
9789811903977
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Overcome Your Kernel Weakness -- What Makes KMMR Unique? -- Acknowledgments -- Contents -- 1 Positive Definite Kernels -- 1.1 Positive Definiteness of a Matrix -- 1.2 Kernels -- 1.3 Positive Definite Kernels -- 1.4 Probability -- 1.5 Bochner's Theorem -- 1.6 Kernels for Strings, Trees, and Graphs -- Appendix -- Exercises 1 sim 15 -- 2 Hilbert Spaces -- 2.1 Metric Spaces and Their Completeness -- 2.2 Linear Spaces and Inner Product Spaces -- 2.3 Hilbert Spaces -- 2.4 Projection Theorem -- 2.5 Linear Operators -- 2.6 Compact Operators -- Appendix: Proofs of Propositions -- Exercises -- 3 Reproducing Kernel Hilbert Space -- 3.1 RKHSs -- 3.2 Sobolev Space -- 3.3 Mercer's Theorem -- Appendix -- Exercises -- 4 Kernel Computations -- 4.1 Kernel Ridge Regression -- 4.2 Kernel Principle Component Analysis -- 4.3 Kernel SVM -- 4.4 Spline Curves -- 4.5 Random Fourier Features -- 4.6 Nyström Approximation -- 4.7 Incomplete Cholesky Decomposition -- Appendix -- Exercises 46sim64 -- 5 The MMD and HSIC -- 5.1 Random Variables in RKHSs -- 5.2 The MMD and Two-Sample Problem -- 5.3 The HSIC and Independence Test -- 5.4 Characteristic and Universal Kernels -- 5.5 Introduction to Empirical Processes -- Appendix -- 6 Gaussian Processes and Functional Data Analyses -- 6.1 Regression -- 6.2 Classification -- 6.3 Gaussian Processes with Inducing Variables -- 6.4 Karhunen-Lóeve Expansion -- 6.5 Functional Data Analysis -- Appendix -- Exercises 83sim100 -- Appendix Bibliography.
Record Nr. UNINA-9910568296803321
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Kernel Methods for Machine Learning with Math and RaKernel methods for machine learning with math and R : 100 exercises for building logic / / Joe Suzuki
Kernel Methods for Machine Learning with Math and RaKernel methods for machine learning with math and R : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (203 pages)
Disciplina 006.31
Soggetto topico R (Computer program language)
Kernel functions
ISBN 9789811903984
9789811903977
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- How to Overcome Your Kernel Weakness -- What Makes KMMR Unique? -- Acknowledgments -- Contents -- 1 Positive Definite Kernels -- 1.1 Positive Definiteness of a Matrix -- 1.2 Kernels -- 1.3 Positive Definite Kernels -- 1.4 Probability -- 1.5 Bochner's Theorem -- 1.6 Kernels for Strings, Trees, and Graphs -- Appendix -- Exercises 1 sim 15 -- 2 Hilbert Spaces -- 2.1 Metric Spaces and Their Completeness -- 2.2 Linear Spaces and Inner Product Spaces -- 2.3 Hilbert Spaces -- 2.4 Projection Theorem -- 2.5 Linear Operators -- 2.6 Compact Operators -- Appendix: Proofs of Propositions -- Exercises -- 3 Reproducing Kernel Hilbert Space -- 3.1 RKHSs -- 3.2 Sobolev Space -- 3.3 Mercer's Theorem -- Appendix -- Exercises -- 4 Kernel Computations -- 4.1 Kernel Ridge Regression -- 4.2 Kernel Principle Component Analysis -- 4.3 Kernel SVM -- 4.4 Spline Curves -- 4.5 Random Fourier Features -- 4.6 Nyström Approximation -- 4.7 Incomplete Cholesky Decomposition -- Appendix -- Exercises 46sim64 -- 5 The MMD and HSIC -- 5.1 Random Variables in RKHSs -- 5.2 The MMD and Two-Sample Problem -- 5.3 The HSIC and Independence Test -- 5.4 Characteristic and Universal Kernels -- 5.5 Introduction to Empirical Processes -- Appendix -- 6 Gaussian Processes and Functional Data Analyses -- 6.1 Regression -- 6.2 Classification -- 6.3 Gaussian Processes with Inducing Variables -- 6.4 Karhunen-Lóeve Expansion -- 6.5 Functional Data Analysis -- Appendix -- Exercises 83sim100 -- Appendix Bibliography.
Record Nr. UNISA-996475763503316
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Sparse estimation with math and Python : 100 exercises for building logic / / Joe Suzuki
Sparse estimation with math and Python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (254 pages)
Disciplina 519.535
Soggetto topico Multivariate analysis
Estimation theory
Python (Computer program language)
ISBN 981-16-1438-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- What makes SEMP unique? -- Contents -- 1 Linear Regression -- 1.1 Linear Regression -- 1.2 Subderivative -- 1.3 Lasso -- 1.4 Ridge -- 1.5 A Comparison Between Lasso and Ridge -- 1.6 Elastic Net -- 1.7 About How to Set the Value of λ -- 2 Generalized Linear Regression -- 2.1 Generalization of Lasso in Linear Regression -- 2.2 Logistic Regression for Binary Values -- 2.3 Logistic Regression for Multiple Values -- 2.4 Poisson Regression -- 2.5 Survival Analysis -- 3 Group Lasso -- 3.1 When One Group Exists -- 3.2 Proxy Gradient Method -- 3.3 Group Lasso -- 3.4 Sparse Group Lasso -- 3.5 Overlap Lasso -- 3.6 Group Lasso with Multiple Responses -- 3.7 Group Lasso Via Logistic Regression -- 3.8 Group Lasso for the Generalized Additive Models -- 4 Fused Lasso -- 4.1 Applications of Fused Lasso -- 4.2 Solving Fused Lasso Via Dynamic Programming -- 4.3 LARS -- 4.4 Dual Lasso Problem and Generalized Lasso -- 4.5 ADMM -- 5 Graphical Models -- 5.1 Graphical Models -- 5.2 Graphical Lasso -- 5.3 Estimation of the Graphical Model Based on the Quasi-Likelihood -- 5.4 Joint Graphical Lasso -- 6 Matrix Decomposition -- 6.1 Singular Decomposition -- 6.2 Eckart-Young's Theorem -- 6.3 Norm -- 6.4 Sparse Estimation for Low-Rank Estimations -- 7 Multivariate Analysis -- 7.1 Principal Component Analysis (1): SCoTLASS -- 7.2 Principle Component Analysis (2): SPCA -- 7.3 K-Means Clustering -- 7.4 Convex Clustering -- Appendix References.
Record Nr. UNINA-9910508455103321
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Sparse estimation with math and Python : 100 exercises for building logic / / Joe Suzuki
Sparse estimation with math and Python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (254 pages)
Disciplina 519.535
Soggetto topico Multivariate analysis
Estimation theory
Python (Computer program language)
ISBN 981-16-1438-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- What makes SEMP unique? -- Contents -- 1 Linear Regression -- 1.1 Linear Regression -- 1.2 Subderivative -- 1.3 Lasso -- 1.4 Ridge -- 1.5 A Comparison Between Lasso and Ridge -- 1.6 Elastic Net -- 1.7 About How to Set the Value of λ -- 2 Generalized Linear Regression -- 2.1 Generalization of Lasso in Linear Regression -- 2.2 Logistic Regression for Binary Values -- 2.3 Logistic Regression for Multiple Values -- 2.4 Poisson Regression -- 2.5 Survival Analysis -- 3 Group Lasso -- 3.1 When One Group Exists -- 3.2 Proxy Gradient Method -- 3.3 Group Lasso -- 3.4 Sparse Group Lasso -- 3.5 Overlap Lasso -- 3.6 Group Lasso with Multiple Responses -- 3.7 Group Lasso Via Logistic Regression -- 3.8 Group Lasso for the Generalized Additive Models -- 4 Fused Lasso -- 4.1 Applications of Fused Lasso -- 4.2 Solving Fused Lasso Via Dynamic Programming -- 4.3 LARS -- 4.4 Dual Lasso Problem and Generalized Lasso -- 4.5 ADMM -- 5 Graphical Models -- 5.1 Graphical Models -- 5.2 Graphical Lasso -- 5.3 Estimation of the Graphical Model Based on the Quasi-Likelihood -- 5.4 Joint Graphical Lasso -- 6 Matrix Decomposition -- 6.1 Singular Decomposition -- 6.2 Eckart-Young's Theorem -- 6.3 Norm -- 6.4 Sparse Estimation for Low-Rank Estimations -- 7 Multivariate Analysis -- 7.1 Principal Component Analysis (1): SCoTLASS -- 7.2 Principle Component Analysis (2): SPCA -- 7.3 K-Means Clustering -- 7.4 Convex Clustering -- Appendix References.
Record Nr. UNISA-996464436203316
Suzuki Joe  
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Sparse estimation with math and R : 100 exercises for building logic / / Joe Suzuki
Sparse estimation with math and R : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (X, 234 p. 54 illus., 46 illus. in color.)
Disciplina 519.535
Soggetto topico Multivariate analysis
ISBN 981-16-1446-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.
Record Nr. UNISA-996464405303316
Suzuki Joe  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Sparse estimation with math and R : 100 exercises for building logic / / Joe Suzuki
Sparse estimation with math and R : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (X, 234 p. 54 illus., 46 illus. in color.)
Disciplina 519.535
Soggetto topico Multivariate analysis
ISBN 981-16-1446-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis.
Record Nr. UNINA-9910495190603321
Suzuki Joe  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical learning with math and python : 100 exercises for building logic / / Joe Suzuki
Statistical learning with math and python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (XI, 256 p. 446 illus., 170 illus. in color.)
Disciplina 519.5
Soggetto topico Mathematical statistics
Logic, Symbolic and mathematical
Python (Computer program language)
ISBN 981-15-7877-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
Record Nr. UNINA-9910495179203321
Suzuki Joe  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical learning with math and python : 100 exercises for building logic / / Joe Suzuki
Statistical learning with math and python : 100 exercises for building logic / / Joe Suzuki
Autore Suzuki Joe
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (XI, 256 p. 446 illus., 170 illus. in color.)
Disciplina 519.5
Soggetto topico Mathematical statistics
Logic, Symbolic and mathematical
Python (Computer program language)
ISBN 981-15-7877-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
Record Nr. UNISA-996464427403316
Suzuki Joe  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui