Bernoulli model

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  • We describe experiments with a Naive Bayes text classifier in the context of anti- spam E-mail filtering, using two different statistical event models: a multi-variate Bernoulli model and a multinomial model. We introduce a family of feature ranking functions for feature selection in the multinomial event model that take account of the word frequency information. We present evaluation results on two publicly available corpora of legitimate and spam E-mails.

    pdf8p bunthai_1 06-05-2013 16 2   Download

  • Recent approaches to text classi cation have used two di erent rst-order probabilistic models for classi cation, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997).

    pdf8p giamdocamnhac 06-04-2013 13 3   Download

  • In banking, especially in risk management, portfolio management, and structured finance, solid quantitative know-how becomes more and more important. We had a two-fold intention when writing this book: First, this book is designed to help mathematicians and physicists leaving the academic world and starting a profession as risk or portfolio managers to get quick access to the world of credit risk management. Second, our book is aimed at being helpful to risk managers looking for a more quantitative approach to credit risk. ...

    pdf285p vigro23 24-08-2012 105 51   Download

  • We analyse the mathematical structure of portfolio credit risk models with particular regard to the modelling of dependence between default events in these models. We explore the role of copulas in latent variable models (the approach that underlies KMV and CreditMetrics) and use non-Gaussian copulas to present extensions to standard industry models. We explore the role of the mixing distribution in Bernoulli mixture models (the approach underlying CreditRisk+) and derive large portfolio approximations for the loss distribution.

    pdf27p enter1cai 12-01-2013 12 3   Download

  • Chapter 9: Words and maps covers global properties of words (N-letter strings from an M-letter alphabet), which are well-studied in classical combinatorics (because they model sequences of independent Bernoulli trials) and in classical applied algorithmics (because they model input sequences for hashing algorithms). The chapter also covers random maps (N-letter words from an N-letter alphabet) and discusses relationships with trees and permutations.

    pdf65p allbymyself_08 22-02-2016 5 1   Download

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