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  1. Sanjeev R. Kulkarni & Gilbert Harman, Statistical Learning Theory: A Tutorial.
    In this article, we provide a tutorial overview of some aspects of statistical learning theory, which also goes by other names such as statistical pattern recognition, nonparametric classification and estimation, and supervised learning. We focus on the problem of two-class pattern classification for various reasons. This problem is rich enough to capture many of the interesting aspects that are present in the cases of more than two classes and in the problem of estimation, and many of the results can be (...)
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  2. Michael K. Miller, Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Wishful Thinking and Social Influence in the 2008 U.S. Presidential Election.
    This paper analyzes individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, ours is the first study of electoral forecasting to involve multiple subnational predictions and to incorporate the influence of respondents’ home states. We relate a range of demographic, political, and cognitive variables to individual accuracy and predictions, as well as to how accuracy improved over time. We find strong support for wishful thinking bias in expectations, as (...)
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  3. Guanchun Wang, Sanjeev R. Kulkarni & Daniel N. Osherson, Aggregating Large Sets of Probabilistic Forecasts by Weighted Coherent Adjustment.
    Stochastic forecasts in complex environments can benefit from combining the estimates of large groups of forecasters (“judges”). But aggregating multiple opinions faces several challenges. First, human judges are notoriously incoherent when their forecasts involve logically complex events. Second, individual judges may have specialized knowledge, so different judges may produce forecasts for different events. Third, the credibility of individual judges might vary, and one would like to pay greater attention to more trustworthy forecasts. These considerations limit the value of simple aggregation (...)
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  4. Gilbert Harman & Sanjeev R. Kulkarni (2006). The Problem of Induction. Philosophy and Phenomenological Research 72 (3):559-575.
    The problem of induction is sometimes motivated via a comparison between rules of induction and rules of deduction. Valid deductive rules are necessarily truth preserving, while inductive rules are not.
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