We consider two algorithms for on-line prediction based on a linear model. The algorithms
are the well-known gradient descent (GD) algorithm and a new algorithm, which we call EG ± …

Kernel-based algorithms such as support vector machines have achieved considerable
success in various problems in batch setting, where all of the training data is available in …

The functional dependency inference problem is the following. Given a relation r, find a set
of functional dependencies that is equivalent with the set of all functional dependencies …

We consider algorithms for combining advice from a set of experts. In each trial, the algorithm
receives the predictions of the experts and produces its own prediction. A loss function is …

We consider adaptive sequential prediction of arbitrary binary sequences when the
performance is evaluated using a general loss function. The goal is to predict on each individual …

We study on-line generalized linear regression with multidimensional outputs, ie, neural
networks with multiple output nodes but no hidden nodes. We allow at the final layer transfer …

We consider online learning in a Reproducing Kernel Hilbert Space. Our method is computationally
efficient and leads to simple algorithms. In particular we derive update equations for …

We give an adversary strategy that forces the Perception algorithm to make(N–k+ 1)/2 mistakes
when learning k-literal disjunctions over N variables. Experimentally we see that even for …

We consider two algorithms for on-line prediction based on a linear model. The algorithms
are the well-known Gradient Descent (GD) algorithm and a new algorithm, which we call EG*. …

We develop an online algorithm called Component Hedge for learning structured concept
classes when the loss of a structured concept sums over its components. Example classes …