Subspace Methods Pattern Recognition (36 results)

Condition: Very Good. *Price HAS BEEN REDUCED by 10% until Monday, Oct. 13 (sale item)* 187 pp., hardcover, light wear to covers else very good. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

24 cm. XVI, 198 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in computational intelligence ; Vol. 552. Sprache: Englisch.

Condition: New.

Condition: New.

Condition: New.

Condition: New.

Paperback. Condition: new. Paperback. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Condition: New. In.

Condition: New. In.

Condition: New.

Hardcover. Condition: new. Hardcover. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PF. Condition: New.

Condition: New. pp. 215.

Condition: New. Editor(s): Chen, Yen-Wei; Jain, Prof. Lakhmi C. Series: Studies in Computational Intelligence. Num Pages: 215 pages, 47 black & white illustrations, 52 colour illustrations, biography. BIC Classification: TBJ; UYQ; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 12. Weight in Grams: 338. . 2016. Softcover reprint of the original 1st ed. 2014. Paperback. . . . .

Condition: New. Editor(s): Chen, Yen-Wei; Jain, Prof. Lakhmi C. Series: Studies in Computational Intelligence. Num Pages: 199 pages, 47 black & white illustrations, 52 colour illustrations, biography. BIC Classification: TBJ; UYQ; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 14. Weight in Grams: 491. . 2014. 2014th Edition. hardcover. . . . .

Taschenbuch. Condition: Neu. Neuware -This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch.

Buch. Condition: Neu. Neuware -This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 216 pp. Englisch.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

Paperback. Condition: Brand New. reprint edition. 216 pages. 9.25x6.10x0.46 inches. In Stock.

Hardcover. Condition: Brand New. 300 pages. 9.25x6.25x0.75 inches. In Stock.

Condition: New. Editor(s): Chen, Yen-Wei; Jain, Prof. Lakhmi C. Series: Studies in Computational Intelligence. Num Pages: 215 pages, 47 black & white illustrations, 52 colour illustrations, biography. BIC Classification: TBJ; UYQ; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 12. Weight in Grams: 338. . 2016. Softcover reprint of the original 1st ed. 2014. Paperback. . . . . Books ship from the US and Ireland.

Condition: New. Editor(s): Chen, Yen-Wei; Jain, Prof. Lakhmi C. Series: Studies in Computational Intelligence. Num Pages: 199 pages, 47 black & white illustrations, 52 colour illustrations, biography. BIC Classification: TBJ; UYQ; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 14. Weight in Grams: 491. . 2014. 2014th Edition. hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Condition: Like New. Like New. book.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: Like New. Like New. book.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: new. Paperback. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condition: new. Hardcover. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis. This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.