Computational Approach to Statistical Learning
-10%
portes grátis
Computational Approach to Statistical Learning
Kane, Michael; Arnold, Taylor; Lewis, Bryan W.
Taylor & Francis Ltd
01/2019
376
Dura
Inglês
9781138046375
15 a 20 dias
662
Descrição não disponível.
Matrix Methods. Direct solutions to linear systems. Iterative linear model solutions. Iteratively reweighted least squares. Blockwise techniques. Convex optimization. Quasi-Newton and gradient descent. Interior point method. Proximal algorithms. Coordinate descent. Active sets and path solutions. Other techniques. Expectation maximization. Model featurization. Neighborhood prediction. Spectral learning. Stochastic techniques.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Kernel Principal Component Analysis;Elastic Net Model;statistical learning;QR Decomposition;optimization;Truncated Power Basis;numerical analysis;Regression Vector;Sgd;Polynomial Kernel;Penalized Regression Models;Principal Component Regression;Kernel Regression;Numeric Vector;Linear Smoothers;Ridge Regression;Lar Algorithm;Elastic Net;Sgd Algorithm;Spectral Clustering;Data Science Programs;Backfitting Algorithm;Negative Binomial;Coordinate Descent;Dimensionality Reduction;Generalized Additive Models;Exponential Family;Sparse Matrix
Matrix Methods. Direct solutions to linear systems. Iterative linear model solutions. Iteratively reweighted least squares. Blockwise techniques. Convex optimization. Quasi-Newton and gradient descent. Interior point method. Proximal algorithms. Coordinate descent. Active sets and path solutions. Other techniques. Expectation maximization. Model featurization. Neighborhood prediction. Spectral learning. Stochastic techniques.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Kernel Principal Component Analysis;Elastic Net Model;statistical learning;QR Decomposition;optimization;Truncated Power Basis;numerical analysis;Regression Vector;Sgd;Polynomial Kernel;Penalized Regression Models;Principal Component Regression;Kernel Regression;Numeric Vector;Linear Smoothers;Ridge Regression;Lar Algorithm;Elastic Net;Sgd Algorithm;Spectral Clustering;Data Science Programs;Backfitting Algorithm;Negative Binomial;Coordinate Descent;Dimensionality Reduction;Generalized Additive Models;Exponential Family;Sparse Matrix
