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Bøger / bibliografier

Bog, 1999

Reinforcement learning : an introduction

Del af Adaptive computation and machine learning

Richard S. Sutton, Andrew G. Barto

Beskrivelse

Summary: An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

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Adaptive computation and machine learning

Bioinformatics : the machine learning approach

Bioinformatics : the machine learning approach

Pierre Baldi

Learning in graphical models

Learning in graphical models

Michael Irwin Jordan

Reinforcement learning : an introduction

Reinforcement learning : an introduction

Richard S. Sutton

Causation, prediction, and search

Causation, prediction, and search

Peter Spirtes

Principles of data mining

Principles of data mining

D.J. Hand

Learning kernel classifiers : theory and algorithms

Learning kernel classifiers : theory and algorithms

Ralf Herbrich

Learning with kernels : support vector machines, regularization, optimization, and beyond

Learning with kernels : support vector machines, regularization, optimization, and beyond

Bernhard Schölkopf

Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning

Carl Edward Rasmussen

The minimum description length principle

The minimum description length principle

Peter D. Grünwald

Introduction to statistical relational learning

Introduction to statistical relational learning

Introduction to natural language processing

Introduction to natural language processing

Jacob Eisenstein

Informationer og udgaver

BogRichard S. SuttonMIT Press