Machine learning and learning theory: PAC learning, algorithmic learning theory, and computational aspects of Bayesian inference and graphical models.
Tag lg.learning refers to the research area of machine learning, and learning theory specifically (falling under arXiv's cs.LG - Learning), as opposed to the general practice of learning. This tag includes the theory of PAC learning, algorithmic learning theory, and computational aspects of Bayesian inference and graphical models. Questions about implementation issues and statistical properties of machine learning systems are more likely to be welcomed at the CrossValidated or MetaOptimize Q&A sites.
The questions must satisfy the usual scope requirements for cstheory as explained in the FAQ.
