9780262047074 - machine learning from weak supervision: an empirical risk minimization approach (adaptive computation and machine learning series) de sugiyama, masashi; bao, han; ishida, takashi; lu, nan; sakai, tomoya (16 resultados)
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
Sugiyama, Masashi,Bao, Han,Ishida, Takashi,Lu, Nan,Sakai, Tomoya
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Librería: Bellwetherbooks, McKeesport, PA, Estados Unidos de AmericaBellwetherbooks
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EUR 43,22
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hardcover. Condición: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
Sugiyama, Masashi,Bao, Han,Ishida, Takashi,Lu, Nan,Sakai, Tomoya
- Tapa dura
Librería: Bellwetherbooks, McKeesport, PA, Estados Unidos de AmericaBellwetherbooks
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EUR 45,12
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hardcover. Condición: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 52,28
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Condición: New. pp. 320.
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 68,60
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Hardcover. Condición: new. Hardcover. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard… machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation. "An overview of machine learning from data that is easily collectible, but challenging to annotate for learning algorithms"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Machine Learning from Weak Supervision : An Empirical Risk Minimization Approach
Sugiyama, Masashi; Bao, Han; Ishida, Takashi; Lu, Nan; Sakai, Tomoya
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 70,68
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Condición: New.
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Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA
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EUR 73,04
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Hardback. Condición: New.
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 66,75
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New. pp. 320.
Machine Learning from Weak Supervision : An Empirical Risk Minimization Approach
Sugiyama, Masashi; Bao, Han; Ishida, Takashi; Lu, Nan; Sakai, Tomoya
- Tapa dura
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
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EUR 80,45
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Condición: As New. Unread book in perfect condition.
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Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 89,10
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Condición: New. 2022. Hardcover. . . . . .
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
Sugiyama, Masashi/ Bao, Han/ Ishida, Takashi/ Lu, Nan/ Sakai, Tomoya
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 99,03
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Hardcover. Condición: Brand New. 320 pages. 9.25x7.25x0.75 inches. In Stock.
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Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
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EUR 110,08
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Condición: New. 2022. Hardcover. . . . . . Books ship from the US and Ireland.
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Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de AmericaRarewaves USA United
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EUR 75,21
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Hardback. Condición: New.
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 114,34
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Hardcover. Condición: new. Hardcover. Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard… machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation. "An overview of machine learning from data that is easily collectible, but challenging to annotate for learning algorithms"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa dura
Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 72,83
Envío por EUR 75,86Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Hardback. Condición: New.
Editorial: Penguin Random House
Librería: INDOO, Avenel, NJ, Estados Unidos de AmericaINDOO
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EUR 60,36
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Condición: As New. Unread copy in mint condition.
Editorial: Penguin Random House
Librería: INDOO, Avenel, NJ, Estados Unidos de AmericaINDOO
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EUR 60,45
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Condición: New. Brand New.
