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Understanding machine learning:
Shalev-Shwartz, Shai
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
006.31 / S528
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
006.31 / S528
