Understanding machine learning: from theory to algorithms /
Shai Shalev-Shwartz, Shai Ben-David.
- primera edición undécima impresión
- Inglaterra: Cambridge University Press, 2019.
- 397 páginas: ilustraciones; 19x26 cm
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
9781107057135
Algoritmos (Computadores) Aprendizaje de las máquinas