Fast Kernel Expansions Applications by Curtò (8 results)
- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 39.69
£ 3.01 shippingShips within U.S.A.Quantity: 4 available
Condition: New.
- Softcover
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 25.75
£ 42.21 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
£ 26.09
£ 60.31 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Fast Kernel Expansions with Applications to CV and DL. Part 1a | Carnegie Mellon. City University of Hong Kong | J. de Curtò | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786203925388 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078…Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 38.03
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 39.60
£ 8.57 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND.
Language: English
Published by LAP LAMBERT Academic Publishing Jun 2021, 2021
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 29.20
£ 19.82 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables th…e use of kernel methods in datasets with a large number of samples. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics. 88 pp. Englisch.
Language: English
Published by LAP LAMBERT Academic Publishing Jun 2021, 2021
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 29.20
£ 51.70 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the us…e of kernel methods in datasets with a large number of samples. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 30.54
£ 52.34 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use… of kernel methods in datasets with a large number of samples. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.
