We have previously introduced the Learn algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance. On three increasingly difficult tests that are specifically designed to com...

We study a common value auction in which two bidders compete for an item the value of which is a function of three independent characteristics. Each bidder observes one of these characteristics, but one of them is “naive” in the sense that he does not realize the other bidder’s signal contains useful information about the item’s value. Therefore, this bidder bids as if this were an Independent ...

Discussion Papers are a series of manuscripts in their draft form. They are not intended for circulation or distribution except as indicated by the author. For that reason Discussion Papers may not be reproduced or distributed without the written consent of the author. This paper studies minimaxity of estimators of a set of linear combinations of location parameters µ i , i = 1,. .. , k under q...

In this work we focus on the following question: how important was the i-th feature in determining the outcome for a given datapoint? We identify a family of influence measures; functions that, given a datapoint ~x, assign a value φi(~x) to every feature i, which roughly corresponds to that i’s importance in determining the outcome for ~x. This family is uniquely derived from a set of axioms: d...

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the per...