9789811216565 - linr algebra & optim appl (v2): volume ii: fundamentals of optimization theory with applications to machine learning: 2 de gallier, jean; jean gallier & jocelyn quaintance; quaintance, jocelyn (22 resultados)
- Tapa dura
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 181,80
Envío por EUR 9,90Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 194,74
Envío por EUR 2,29Se envía dentro de Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: New.
- Tapa dura
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 202,96
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 2 disponibles
HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: World Scientific Publishing Company 2020-05-23, 2020
- Tapa dura
Librería: Chiron Media, Wallingford, Reino UnidoChiron Media
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 192,16
Envío por EUR 18,07Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Hardcover. Condición: New.
- Tapa dura
Librería: Speedyhen, Hertfordshire, Reino UnidoSpeedyhen
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 162,51
Envío por EUR 47,82Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: NEW.
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 210,36
Envío por EUR 2,29Se envía dentro de Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa dura
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 195,90
Envío por EUR 17,50Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa dura
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 199,43
Envío por EUR 13,97Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. In.
- Tapa dura
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 207,74
Envío por EUR 17,50Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
Idioma: Inglés
Editorial: World Scientific Publishing Co Pte Ltd, Singapore, 2020
- Tapa dura
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 226,84
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: new. Hardcover. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applyin…g these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Tapa dura
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 241,42
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardback. Condición: New. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these tech…niques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
- Tapa dura
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 233,52
Envío por EUR 9,50Se envía de Irlanda a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: New. 2020. Hardcover. . . . . .
- Tapa dura
Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 255,99
Gastos de envío gratisSe envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardback. Condición: New. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these tech…niques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
- Tapa dura
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 205,82
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: New. KlappentextrnrnVolume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematica.
- Tapa dura
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA United
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 244,55
Envío por EUR 43,36Se envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardback. Condición: New. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these tech…niques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
- Tapa dura
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 283,49
Envío por EUR 17,50Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Hardcover. Condición: Brand New. 877 pages. 9.50x6.50x1.25 inches. In Stock.
- Tapa dura
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 296,19
Envío por EUR 9,10Se envía dentro de Estados Unidos de AmericaCantidad disponible: 2 disponibles
Condición: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.
- Tapa dura
Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 241,74
Envío por EUR 75,81Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardback. Condición: New. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these tech…niques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
Idioma: Inglés
Editorial: World Scientific Publishing Co Pte Ltd, Singapore, 2020
- Tapa dura
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 334,02
Envío por EUR 32,08Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Hardcover. Condición: new. Hardcover. Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applyin…g these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa dura
- Impresión bajo demanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 240,24
Envío por EUR 17,50Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Hardcover. Condición: Brand New. 877 pages. 9.50x6.50x1.25 inches. In Stock. This item is printed on demand.
- Tapa dura
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 230,27
Envío por EUR 67,11Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnin…gs of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.
Más imágenes- Tapa dura
- Impresión bajo demanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 267,80
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Buch. Condición: Neu. LINR ALGEBRA & OPTIM APPL (V2) | Gallier Jean | Buch | Gebunden | Englisch | 2020 | World Scientific | EAN 9789811216565 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
