Predictive Filtering and Smoothing of Short Records by using Maximum Entropy
نویسندگان
چکیده
منابع مشابه
Maximum Entropy for Collaborative Filtering
Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higher-order interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables dur...
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This paper discusses various aspects of smoothing techniques in maximum entropy language modeling, a topic not sufficiently covered by previous publications. We show (1) that straightforward maximum entropy models with nested features, e.g. tri–, bi–, and unigrams, result in unsmoothed relative frequencies models; (2) that maximum entropy models with nested features and discounted feature count...
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A Maximum Entropy Approach for Collaborative Filtering
Collaborative filtering (CF) involves predicting the preferences of a user for a set of items given partial knowledge of the user’s preferences for other items, while leveraging a database of profiles for other users. CF has applications e.g. in predicting Web sites a person will visit and in recommending products. Fundamentally, CF is a pattern recognition task, but a formidable one, often inv...
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Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributi...
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ژورنال
عنوان ژورنال: Geophysical Journal International
سال: 1973
ISSN: 0956-540X,1365-246X
DOI: 10.1093/gji/35.1.380-a