ChanningArndt ShermanRobinson FinnTarp Parameter estimation for a computable general equilibrium model: a maximum entropy approach Economic Modelling 19 375–398 2002 AdamL.Berger StephenDellaPietra VincentJ. DellaPietra A Maximum Entropy Approach to Natural Language Processing Computational Linguistics 22 39-71 1996 JamieMarconi JamesA.Foster A Hard Problem for Genetic Algorithms: Finding Cliques in Keller Graphs IEEE Press 650–655 1998 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. JasonD. M.Rennie Improving Multi-class Text Classification with Naive Bayes 2001 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. SantoshC.Sutradhar NagarajK.Neerchal JorgeG.Morel A goodness-of-fit test for overdispersed binomial (or multinomial) models Journal of Statistical Planning and Inference 138 1459–1471 2008 M.L.Tiku R.P.Suresh A new method of estimation for location and scale parameters Journal of Statistical Planning and Inference 30 281–292 1992 RobertoTogneri JeffMa LiDeng Parameter estimation of a target-directed dynamic system model with switching states Signal Processing 81 975–987 2001 Siu-KeungTse On the existence and uniqueness of maximum likelihood estimates in polytomous response models Journal of Statistical Planning and Inference 14 269–273 1986 PaulYip DanielY. T.Fong Estimating population size from a removal experiment Statistics & Probability Letters 16 129–135 1993 GangZheng RezaModarres A robust estimate of the correlation coefficient for bivariate normal distribution using ranked set sampling Journal of Statistical Planning and Inference 136 298–309 2006