Für diesen Artikel ist leider kein Bild verfügbar.

Machine Learning in R

Alexandros Karatzoglou (Autor)

Buch | Hardcover

304 Seiten

2023
Productivity Press (Verlag)
978-1-4987-2643-6 (ISBN)

Titel ist leider vergriffen;
keine Neuauflage

Artikel merken

This book provides a practical, hands-on introduction to the use of R in machine learning. Both supervised and unsupervised learning are comprehensively covered in the book, and the book explores key topics in machine learning, including support vector machines and neural networks. The book also describes various machine learning R packages and includes example code and use cases throughout.

Introduction to the Book. Basics of R. Introduction to Machine Learning With a Bit of History. Partitioning Methods and Trees. Regression and Classification Trees (Rpart, Tree Packages). Random Forests (Random Forests and Ipred, Packages). Support Vector Machines and Kernel Methods. SVM’s (Package Kernlab, e1071). KPCA (Package Kernlab). Spectral Clustering (Kernlab). Boosting. Generalized Boosting Models (Classification, Regression, Ranking In Package GBM). Neural Networks and Deep Learning. Feed-Forward Networks (Package Nnet). Restricted Boltzmann Machines. Deep Belief Networks. Autoencoders (Stacked). Multilayer Feed-Forward Networks (Package Deepnet, Darch, H20). Bayesian Methods. Naïve Bayes (Package e1071). Topic Modeling. Latent Dirichlet Allocation (Package Topic Modeling). Clustering. Hierarchical Clustering. Partitioning Clustering. Model-Based Clustering. Model Selection and Evaluation. Tune Parameters (Function Tune In e1071). Evaluating Models (Package ROCR, etc.).

Erscheinungsdatum	25.05.2016
Verlagsort	Portland
Sprache	englisch
Maße	156 x 234 mm
Themenwelt	Mathematik / Informatik ► Informatik ► Datenbanken
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik
	Technik ► Elektrotechnik / Energietechnik
ISBN-10	1-4987-2643-7 / 1498726437
ISBN-13	978-1-4987-2643-6 / 9781498726436
Zustand	Neuware