%0 
%0 Journal Article
%A Arndt, Channing; Robinson, Sherman & Tarp, Finn
%D 2002
%T Parameter estimation for a computable general equilibrium model: a maximum entropy approach
%E 
%B Economic Modelling
%C 
%I 
%V 19
%6
%N 3
%P 375--398
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%F Arndt2002
%K Maximum entropy 
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%U http://www.sciencedirect.com/science/article/B6VB1-45943C4-3/1/f6e08d14b6b356ec0562ba18d807e0e5
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%0 
%0 Journal Article
%A Berger, Adam L.; Pietra, Stephen Della & Pietra, Vincent J. Della
%D 1996
%T A Maximum Entropy Approach to Natural Language Processing
%E 
%B Computational Linguistics
%C 
%I 
%V 22
%6
%N 1
%P 39-71
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%F berger96maximum
%K entropy imported maximum nlp 
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%U citeseer.ist.psu.edu/berger96maximum.html
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%0 
%0 Conference Proceedings
%A Marconi, Jamie & Foster, James A.
%D 1998
%T A Hard Problem for Genetic Algorithms: Finding Cliques in Keller Graphs
%E 
%B Proceedings of the 1998 IEEE World Congress on Computational Intelligence
%C Anchorage, Alaska, USA
%I IEEE Press
%V 
%6
%N 
%P 650--655
%& 
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%S 
%7
%8 5-9 May
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%F marconi:1998:hpGAfckg
%K Keller algorithms, clique, complexity conjecture, genetic graphs, hardness, maximum programming, 
%X We present evidence that finding the maximum clique in Keller graphs is an example of a family of problems which are both natural and inherently difficult for genetic algorithms. Specifically, we employ a hybrid genetic algorithm to find the largest clique in Keller graphs. We present theoretical reasons why this problem is likely to be particularly hard for this family of graphs. Our results confirm this suspicion. We then discuss several characteristics of this graph family which confound genetic algorithms: its uniformity, edge density and small diameter.
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%0 
%0 Thesis
%A Rennie, Jason D. M.
%D 2001
%T Improving Multi-class Text Classification with Naive Bayes
%E 
%B 
%C 
%I Massachusetts Institute of Technology
%V 
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%F rennie2001naive
%K bayes deduction estimation exhaustive herleitung komplett likelihood map maximum mle multinomial naive prior thesis 
%X There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.
%Z 
%U http://people.csail.mit.edu/~jrennie/papers/sm-thesis.pdf
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%0 
%0 Journal Article
%A Sutradhar, Santosh C.; Neerchal, Nagaraj K. & Morel, Jorge G.
%D 2008
%T A goodness-of-fit test for overdispersed binomial (or multinomial) models
%E 
%B Journal of Statistical Planning and Inference
%C 
%I 
%V 138
%6
%N 5
%P 1459--1471
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%F Sutradhar2008
%K Maximum estimation likelihood 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V0M-4P8GWX9-1/1/c2840994a66c496770d8dd54ed0dcc3c
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%0 
%0 Journal Article
%A Tiku, M. L. & Suresh, R. P.
%D 1992
%T A new method of estimation for location and scale parameters
%E 
%B Journal of Statistical Planning and Inference
%C 
%I 
%V 30
%6
%N 2
%P 281--292
%& 
%Y 
%S 
%7
%8 Feb
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%F Tiku1992
%K Modified likelihood maximum 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V0M-45W39N7-10/1/f204bef8ea55aa6357c629fab7078223
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%0 
%0 Journal Article
%A Togneri, Roberto; Ma, Jeff & Deng, Li
%D 2001
%T Parameter estimation of a target-directed dynamic system model with switching states
%E 
%B Signal Processing
%C 
%I 
%V 81
%6
%N 5
%P 975--987
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%F Togneri2001
%K Maximum likelihood 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V18-430G6WG-7/1/d41687de682331d48b07b49e2695922b
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%0 
%0 Journal Article
%A Tse, Siu-Keung
%D 1986
%T On the existence and uniqueness of maximum likelihood estimates in polytomous response models
%E 
%B Journal of Statistical Planning and Inference
%C 
%I 
%V 14
%6
%N 2-3
%P 269--273
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%F Tse1986b
%K Maximum estimators likelihood 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V0M-48HRX14-F/1/a196a264d6f13d6ac33af7f338405407
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%0 
%0 Journal Article
%A Yip, Paul & Fong, Daniel Y. T.
%D 1993
%T Estimating population size from a removal experiment
%E 
%B Statistics \& Probability Letters
%C 
%I 
%V 16
%6
%N 2
%P 129--135
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%F Yip1993
%K Maximum estimation likelihood 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V1D-45D9T5H-7/1/2dc58b7072f3b63f4fc022e9cd93574f
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%0 
%0 Journal Article
%A Zheng, Gang & Modarres, Reza
%D 2006
%T A robust estimate of the correlation coefficient for bivariate normal distribution using ranked set sampling
%E 
%B Journal of Statistical Planning and Inference
%C 
%I 
%V 136
%6
%N 1
%P 298--309
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%F Zheng2006
%K Modified estimation likelihood maximum 
%X 
%Z 
%U http://www.sciencedirect.com/science/article/B6V0M-4CXRYSW-B/1/0cba9b32bc72461925be7f09b86c7fa7
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