We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, that is, the hypothesis that minimizes the training error, our algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the ...

Lagrangian interpolation is a classical way to approximate general functions by finite sums of well chosen, pre-definite, linearly independent generating functions; it is much simpler to implement than determining the best fits with respect to some Banach (or even Hilbert) norms. In addition, only partial knowledge is required (here values on some set of points). The problem of defining the bes...

Different approaches in machine translation achieve similar translation quality with a variety of translations in the output. Recently it has been shown, that it is possible to leverage the individual strengths of various systems and improve the overall translation quality by combining translation outputs. In this paper we present a method of hypothesis selection which is relatively simple comp...

The types of problems in data association for tracking are (i) measurement-to-measurement association (M2MA), i.e., track initiation, (ii) measurement-to-track association (M2TA), i.e., track continuation, (iii) trackto-track association (T2TA), for track fusion. Among the M2TA algorithms, it is well known that the Multiple Hypothesis Tracker (MHT) performs, due its time window, much better tha...