integer linear optimization models
NON-LINEAR REGRESSION MODELS
... Multivariate models Simultaneous equations models 5.1 Non -linear 5.2 Other single equation two-stage least squares estimator 5.3 Non -linear simultaneous 5.4 Non -linear three-stage 5.5 Non -linear full ... important topics which, although non-linearity is involved, woud best be studied within another context, e.g non -linear error-in-variable models and non -linear time-series models Regarding these two topics, ... references on non -linear regression models Malinvaud (1970b) devotes one long chapter to non -linear regression models in which he discusses the asymptotic properties of the nonlinear least squares...
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Tài liệu Báo cáo khoa học: "Concise Integer Linear Programming Formulations for Dependency Parsing" pptx
... called an integer linear program (ILP) For some special parameter settings—e.g., when b is an integer vector and A is totally unimodular5 —all vertices of the constraining polyhedron are integer ... via integer linear programming inference In Proc of COLING M Richardson and P Domingos 2006 Markov logic networks Machine Learning, 62(1):107–136 S Riedel and J Clarke 2006 Incremental integer linear ... Princeton University Press D Roth and W T Yih 2005 Integer linear programming inference for conditional random fields In ICML A Schrijver 2003 Combinatorial Optimization: Polyhedra and Efficiency, volume...
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Tài liệu Báo cáo khoa học: "Computationally Efficient M-Estimation of Log-Linear Structure Models∗" doc
... distribution over X × Y HMMs and PCFGs, while less accurate as predictors than the rich-featured log -linear models we desire, might be used to define q0 The model we estimate will have the form pw (x, ... term “M-estimation” to refer to the minimization of (w) as a way of training a log -linear model Algorithms for Models of Sequences and Trees The first step is to compute path-sums into and out of ... structure’s leaves 4.2 ∂ =− ∂wj Optimization To carry out M-estimation, we minimize the function (w) in Eq To apply gradient descent or a quasi-Newton numerical optimization method,6 it suffices...
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Báo cáo khoa học: "Grammatical Role Labeling with Integer Linear Programming" pot
... the RANLP 2005 Vasin Punyakanok, Dan Roth, Wen-tau Yih, and Dave Zimak 2004 Role Labeling via Integer Linear Programming Inference Proceedings of the 20th COLING Dan Roth and Wen-tau Yih 2005 ILP ... for fruitful (“long distance”) discussions and a number of (steadily improved) maximum entropy models Also, the detailed comments of the reviewers have been very helpful References Ernst Althaus,...
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Linear Factor Models in Finance docx
... different measures of sensitivity to the linear effects of the chosen factors as well as a time-varying non -linear component 2.1 Introduction Linear factor models are used universally within the ... Dr Wilding has developed new optimization techniques and robust estimation routines to fit several types of factor model Introduction This book on linear factor models starts with an introductory ... central position that linear factor models hold in portfolio formation and risk management John Knight and Stephen Satchell Review of literature on multifactor asset pricing models Mario Pitsillis...
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Báo cáo khoa học: "Recognizing Authority in Dialogue with an Integer Linear Programming Constrained Model" pptx
... the current and previous contribution 4.3 Constraints using Integer Linear Programming We formulate our constraints using Integer Linear Programming (ILP) This formulation has an advantage over ... automatic classifier, performance will increase In section 4.3 we formalize these constraints using Integer Linear Programming 4.1 g A2 go almost to the edge of the lake f A1 yeah Without this context, ... describing what a correct label sequence looks like, and are imposed on our model using an Integer Linear Programming formulation (Roth and Yih, 2004) In section 5, this model is evaluated on...
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convex analysis and non linear optimization theory and examples - borwein,lewis
... modern analysis: from linear to nonlinear analysis, from smooth to nonsmooth, and from the study of functions to multifunctions Thus although we use certain optimization models repeatedly to illustrate ... repeatedly to illustrate the main results (models such as linear and semidefinite programming duality and cone polarity), we constantly emphasize the power of abstract models and notation Good reference ... sets are convex Given any set D ⊂ E, the linear span of D, denoted span (D), is the smallest linear subspace containing D It consists exactly of all linear combinations of elements of D Analogously,...
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Báo cáo hóa học: " Research Article Comparison of Linear Prediction Models for Audio Signals" pdf
... become extremely large ALTERNATIVE LINEAR PREDICTION MODELS In this section, we present five existing alternative LP models, and we illustrate how all these models attempt to compensate for the ... WLP, SLP models and the PZLP and PLP models cascaded with a conventional LP model are quite competitive, however, toward higher pitch values, the HOLP and WLP models outperform the other models ... (PZLP) linear prediction models have not been applied to audio modeling before, however, some speech analysis techniques rely on a PZLP model [40–42] All considered approaches result in stable LP models, ...
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Báo cáo sinh học: "Heterogeneous variances in Gaussian linear mixed models" pot
... numerical optimization in multivariate REML (co)variance component estimation Genet Sel Evol 26, 537-545 Lindstrom MJ, Bates DM (1988) Newton-Raphson and EM algorithms for linear mixedeffects models ... using GLIM Appl Stat 36, 332-339 Cantet RJC (1990) Estimation and prediction problems in mixed linear models for maternal genetic effects PhD thesis, University of Illinois, Urbana, IL, USA Dempster ... Im S (1990) A method for assessing extent and of heterogeneity of residual variances in mixed linear models J Dairy Sci 73, 1612-1624 Foulley JL, San Cristobal M, Gianola D, Im S (1992) Marginal...
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Báo cáo sinh học: "Use of linear mixed models for genetic evaluation of gestation length and birth weight allowing for heavy-tailed residual effects" pptx
... with Student-t mixed linear models: a simulation study Genet Sel Evol 1998, 30:565-583 doi:10.1186/1297-9686-42-26 Cite this article as: Kizilkaya et al.: Use of linear mixed models for genetic ... will be associated with fitted models that are better at predicting breeding values than models with low correlations and high prediction error variance Some fitted models might be significantly ... the true model was BN, all fitted models performed identically In general, the accuracy and PEV results from BSt and BN models suggest that heavy-tailed fitted models can improve accuracy and PEV...
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Báo cáo sinh học: "A marginal quasi-likelihood approach to the analysis of Poisson variables with generalized linear mixed models" pot
... (1984) Extra-Poisson variation in log -linear models Appl Stat 33, 38-44 Breslow NE, Clayton DG (1992) Approximate Inference in Generalixed Linear Mixed Models Tech Rep No 106, Univ Washington, ... 393-408 Henderson CR (1984) Applications of linear models in animal breeding Univ Guelph, Guelph, Ont Hinde JP (1982) Compound Poisson regression models In: GLIM 82 (Gilchrist R, ed) Springer ... non -linear models In: Advances in Statistical Methods for Genetic Improvement of Livestock (Gianola D, Hammond K, eds) Springer-Verlag, Heidelberg, 190-206 Solomon PJ, Cox DR (1992) Non linear...
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1 component mode synthesis and polynomial chaos expansions for stochastic frequency functions of large linear FE models
... used to investigate the frequency transfer functions for large linear FE models of beams and assembled plates with linear and nonlinear stochastic parameters The random frequency transfer function ... systems with linear and nonlinear random parameters The proposed methodological approach proves to be of particular advantage and can be improved by higher polynomial chaos for strong nonlinear stochastic ... order to demonstrate the efficiency of this method, some benchmark tests are analyzed with linear and nonlinear parameters For the sake of accuracy and comparison four methodological approaches...
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Analysis of generalized linear mixed models
... Linear Models 3.1 3.2 3.3 3.4 35 Introduction Inference in Generalized Linear Models Diagnostics and Model Fit Generalized Linear Modeling versus Transformations 35 37 46 52 Chapter Linear Mixed Models ... for generalized linear models appeared in the mid 1980s, the first so ware for linear mixed models in the 1990s, and the first truly usable so ware for generalized linear mixed models appeared ... Biometrics 42:121–130 doi:10.2307/2531248 CHAPTER GENERALIZED LINEAR MODELS 3.1 INTRODUCTION Generalized linear models extend normal theory linear models to response variables whose distributions belong...
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Variable selection procedures in linear regression models
... VARIABLE SELECTION PROCEDURES IN LINEAR REGRESSION MODELS XIE YANXI (B.Sc National University of Singapore) A THESIS SUBMITTED FOR THE DEGREE ... 52 3.2 Literature Review 53 3.2.1 Partial Linear models 53 3.2.2 H-likelihood 62 Variable Selection ... learning models, under small n and large d situation By removing most irrelevant and redundant variables from the data, variable selection helps improve the performance of learning models in...
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Tài liệu Modelling the e®ects of air pollution on health using Bayesian Dynamic Generalised Linear Models pdf
... analysis uses Poisson linear or additive models In this paper we use a Bayesian dynamic generalised linear model (DGLM) to estimate this relationship, which allows the standard linear or additive ... and Tutz (2001)) to analyse air pollution and health data Dynamic generalised linear models extend generalised linear models by allowing the regression parameters to evolve over time via an autoregressive ... suggests areas for future work Bayesian Dynamic generalised linear models A Bayesian dynamic generalised linear model extends a generalised linear model by allowing a subset of the regression parameters...
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Báo cáo khoa học: "Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty" potx
... for L1-regularized loglinear models Experimental results are presented in Section Some related work is discussed in Section Section gives some concluding remarks Log -Linear Models In this section, ... algorithm pact and accurate models much more quickly than the OWL-QN algorithm This paper is organized as follows Section provides a general description of log -linear models used in NLP Section ... remarks Log -Linear Models In this section, we briefly describe log -linear models used in NLP tasks and L1 regularization A log -linear model defines the following probabilistic distribution over...
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Báo cáo khoa học: "Beyond Log-Linear Models: Boosted Minimum Error Rate Training for N-best Re-ranking" docx
... hypotheses Modeling problem: Log -linear model insufficient? Optimization problem: Stuck in local optimum? BLEU=.40, achieved by re-ranking with MERT Figure 1: Both modeling and optimization problems increase ... is more serious for log -linear models of around 10 features and focus on it in this work To truly achieve the benefits of re-ranking in MT, one must go beyond the log -linear model The reranker ... difference of BLEU The final classifier f T can be seen as a voting procedure among multiple log -linear models generated by MERT The weighted vote for hypotheses in an N-best list xi is represented...
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Báo cáo khoa học: "Contrastive Estimation: Training Log-Linear Models on Unlabeled Data∗" potx
... syntactic model should make it so Log -Linear Models We have not yet specified the form of our probabilistic model, only that it is parameterized by θ ∈ Rn Log -linear models, which we will show are a ... (upper box) and unsupervised (lower box) estimation with log -linear models in terms of Eq where Ai ⊂ Bi (for each i) For log -linear models this is simply (x,y)∈Ai i (x,y)∈Bi u x, y | θ (6) u x, ... most models were the best on their own criterion, and among unsupervised models, L ENGTH performed best on the CL criterion 360 RB∗ →ADV) To take better advantage of the power of loglinear models specifically,...
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