9780262047074 - machine learning from weak supervision: an empirical risk minimization approach di sugiyama, masashi; bao, han; ishida, takashi; lu, nan; sakai, tomoya (17 risultati)
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
- Rilegato
Da: Bellwetherbooks, McKeesport, PA, U.S.A.Bellwetherbooks
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Molto buono
EUR 44,32
EUR 3,48 spedizioneSpedito in U.S.A.Quantità: 2 disponibili
hardcover. Condizione: 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
- Rilegato
Da: Bellwetherbooks, McKeesport, PA, U.S.A.Bellwetherbooks
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Ottimo
EUR 46,50
EUR 3,48 spedizioneSpedito in U.S.A.Quantità: 2 disponibili
hardcover. Condizione: Fine. LIKE NEW!!! Has a red or black remainder mark on bottom/exterior edge of pages.
- Rilegato
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 53,22
EUR 3,51 spedizioneSpedito in U.S.A.Quantità: 3 disponibili
Condizione: New. pp. 320.
- Rilegato
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 69,84
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: 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.Standar…d 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
- Rilegato
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 71,96
EUR 2,33 spedizioneSpedito in U.S.A.Quantità: 15 disponibili
Condizione: New.
- Rilegato
Da: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 74,36
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Hardback. Condizione: New.
- Rilegato
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 66,62
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 3 disponibili
Condizione: New. pp. 320.
Machine Learning from Weak Supervision : An Empirical Risk Minimization Approach
Sugiyama, Masashi; Bao, Han; Ishida, Takashi; Lu, Nan; Sakai, Tomoya
- Rilegato
Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 81,64
EUR 2,33 spedizioneSpedito in U.S.A.Quantità: 15 disponibili
Condizione: As New. Unread book in perfect condition.
- Rilegato
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 89,10
EUR 10,50 spedizioneSpedito da Irlanda a U.S.A.Quantità: 15 disponibili
Condizione: New. 2022. Hardcover. . . . . .
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
Sugiyama, Masashi/ Bao, Han/ Ishida, Takashi/ Lu, Nan/ Sakai, Tomoya
- Rilegato
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 100,30
EUR 14,49 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: Brand New. 320 pages. 9.25x7.25x0.75 inches. In Stock.
- Rilegato
Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 93,44
EUR 21,18 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 15 disponibili
Hardback. Condizione: New. New copy - Usually dispatched within 7-11 working days.
- Rilegato
Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 112,07
EUR 9,25 spedizioneSpedito in U.S.A.Quantità: 15 disponibili
Condizione: New. 2022. Hardcover. . . . . . Books ship from the US and Ireland.
- Rilegato
Da: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 76,36
EUR 44,04 spedizioneSpedito in U.S.A.Quantità: Più di 20 disponibili
Hardback. Condizione: New.
- Rilegato
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 113,63
EUR 32,59 spedizioneSpedito da Australia a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: 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.Standar…d 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.
- Rilegato
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 73,35
EUR 75,36 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
Hardback. Condizione: New.
Editore: Penguin Random House
Da: INDOO, Avenel, NJ, U.S.A.INDOO
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 61,45
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: As New. Unread copy in mint condition.
Editore: Penguin Random House
Da: INDOO, Avenel, NJ, U.S.A.INDOO
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 61,55
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Brand New.
