Abstract. Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) prob...
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Secret-key agreement between two parties Alice and Bob, connected by an insecure channel, can be realized in an informationtheoretic sense if the parties share many independent pai...
Since Val Tannen's pioneering work on the combination of simply-typed λ-calculus and rst-order rewriting [11], many authors have contributed to this subject by extending it ...
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...