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Understanding machine learning: from theory to algorithms / Shai Shalev-Shwartz, Shai Ben-David.
Tipo de material:
TextoIdioma: Inglés Detalles de publicación: Inglaterra: Cambridge University Press, 2019.Edición: primera edición undécima impresiónDescripción: 397 páginas: ilustraciones; 19x26 cmISBN: - 9781107057135
- 006.31 S528
Contenidos:
Fundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity trade -off -- The vc-dimension -- Nonuniform learnability -- The runtime of learning -- From therory to algorithms -- linear predictors -- Boosting -- Model selection and validation -- Convex learning problems -- Regularization and stability -- Stochastic gradient descent -- Support vector machines -- Kernel methods -- Multiclass, ranking, and complex prediction problems -- Decision trees -- Nearest neighbor -- Neural networks -- Additional learning models -- Online learning -- Clustering -- Dimensionality reduciton -- Generative models -- Feature selection and generatio -- Advanced theory -- Redamacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-bayes
| Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Estado | Código de barras | |
|---|---|---|---|---|---|---|
Libros
|
Biblioteca Jorge Franco Vélez Colección General | General | 006.31 S528 Ej.1 (Navegar estantería(Abre debajo)) | Disponible | 20804 |
Total de reservas: 0
Referencias: páginas 385-393
Fundations -- A gentle start -- A formal learning model -- Learning via uniform convergence -- The bias-complexity trade -off -- The vc-dimension -- Nonuniform learnability -- The runtime of learning -- From therory to algorithms -- linear predictors -- Boosting -- Model selection and validation -- Convex learning problems -- Regularization and stability -- Stochastic gradient descent -- Support vector machines -- Kernel methods -- Multiclass, ranking, and complex prediction problems -- Decision trees -- Nearest neighbor -- Neural networks -- Additional learning models -- Online learning -- Clustering -- Dimensionality reduciton -- Generative models -- Feature selection and generatio -- Advanced theory -- Redamacher complexities -- Covering numbers -- Proof of the fundamental theorem of learning theory -- Multiclass learnability -- Compression bounds -- PAC-bayes
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