Bemærk: Kan leveres før jul.
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues.
Bemærk: Kan ikke leveres før jul.
This textbook introduces linear algebra and optimization in the context of machine learning. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra.
Bemærk: Kan ikke leveres før jul.
These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan leveres før jul.
Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.
Bemærk: Kan leveres før jul.
This book covers probability and statistics from the machine learning perspective. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
Bemærk: Kan ikke leveres før jul.
This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. The authors... Læs mere
Bemærk: Kan ikke leveres før jul.