Editorial Aims

The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research. Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem. Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends^® in Machine Learning publishes high-quality survey and tutorial monographs of the field.

Each issue of Foundations and Trends ^® in Machine Learning comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.

Editorial Scope

Foundations and Trends^® in Machine Learning publishes survey and tutorial articles on the theory, algorithms and applications of machine learning, including the following topics:

• Adaptive control and signal processing
• Applications and case studies
• Behavioral, cognitive and neural learning
• Bayesian learning
• Classification and prediction
• Clustering
• Data mining
• Dimensionality reduction
• Evaluation
• Game theoretic learning
• Graphical models
• Independent component analysis
• Inductive logic programming
• Kernel methods
• Markov chain Monte Carlo
• Model choice
• Nonparametric methods
• Online learning
• Optimization
• Reinforcement learning
• Relational learning
• Robustness
• Spectral methods
• Statistical learning theory
• Variational inference
• Visualization