WorldCat Identities

Lafferty, John

Overview
Works: 25 works in 47 publications in 1 language and 686 library holdings
Genres: Drama  Biography  Conference proceedings  Military history  Pictorial works  Fiction 
Roles: Film editor, Editor
Classifications: PN1997.C554, 791.4372
Publication Timeline
Key
Publications about  John Lafferty Publications about John Lafferty
Publications by  John Lafferty Publications by John Lafferty
Most widely held works about John Lafferty
 
Most widely held works by John Lafferty
Prediction and discovery : AMS-IMS-SIAM Joint Summer Research Conference, Machine and Statistical Learning : Prediction and Discovery, June 25-29, 2006, Snowbird, Utah by AMS-IMS-SIAM Joint Summer Research Conference Machine and Statistical Learning : Prediction and Discovery ( Book )
7 editions published in 2007 in English and held by 176 WorldCat member libraries worldwide
Language modeling for information retrieval by W. Bruce Croft ( Book )
9 editions published between 2003 and 2011 in English and held by 137 WorldCat member libraries worldwide
This book contains the first collection of papers addressing recent developments in the design of information retrieval systems using language modeling techniques. Language modeling approaches are used in a variety of other language technologies, such as speech recognition and machine translation, and the book shows that applications such as Web search, cross-lingual search, filtering, and summarization can be described in the same formal framework. The book is intended primarily for researchers and advanced graduate students working in the language technologies areas of computer science or information science
DC 9/11 time of crisis ( Visual )
2 editions published in 2004 in English and held by 115 WorldCat member libraries worldwide
Focuses on the difficult decisions and tasks faced by President George Bush and his staff on September 11, 2001 and the days following the attacks. Based on in-depth interviews and extensive research. Recounts the tragic events from the moment Bush hears the news of the attacks to significant briefings with advisors. Chronicles national security meetings, links with Osama bin Laden and the al Qaeda network. Illustrates the Administrations strategy for responding both the the terrorists and the American people
Asunder ( Visual )
2 editions published in 2003 in English and held by 109 WorldCat member libraries worldwide
Chance and his very pregnant wife, Roberta, happily board a Ferris wheel with their best friends Michael and his fashion designer wife, Lauren, when a freak accident strikes, and Roberta and the baby are killed. Michael and Lauren welcome Chance into their luxurious home to grieve. Soon Lauren reveals that she recently had a secret abortion because she did not know if the child was Michael's or Chance's. Grief-stricken and jealous, Chance starts stalking Lauren and doing everything in his power to wreck her marriage
Christmas child ( Visual )
1 edition published in 2010 in English and held by 98 WorldCat member libraries worldwide
As Christmas draws near, Jack finds himself disconnecting from the holidays, his job, and his wife. While out of town for business, he uncovers secrets from his past, reunites with the family he never knew, and returns to the love that never left him
The haunted world of Edward D. Wood, Jr ( Visual )
1 edition published in 1997 in English and held by 12 WorldCat member libraries worldwide
The story of Hollywood director, Edward D. Wood, Jr., accompanied by interviews with those who knew him and film clips
Ordered Binary Decision Diagrams and Minimal Trellises by John D Lafferty ( Book )
2 editions published in 1998 in English and held by 3 WorldCat member libraries worldwide
Ordered binary decision diagrams (OBDDs) are graph based data structures for representing Boolean functions. They have found widespread use in computer aided design and in formal verification of digital circuits. Minimal trellises are graphical representations of error correcting codes that play a prominent role in coding theory. This paper establishes a close connection between these two graphical models, as follows. Let C be a binary code of length n, and let fc(x1, ..., xn) be the Boolean function that takes the value 0 at x1, ..., xn if and only if (x1, ..., xn)epsilonC. Given this natural one to one correspondence between Boolean functions and binary codes, we prove that the minimal proper trellis for a code C with minimum distance d> 1 is isomorphic to the single terminal OBDD for its Boolean indicator function fC(x1, ..., xn). Prior to this result, the extensive research during the past decade on binary decision diagrams in computer engineering and on minimal trellises in coding theory has been carried out independently. As outlined in this work, the realization that binary decision diagrams and minimal trellises are essentially the same data structure opens up a range of promising possibilities for transfer of ideas between these disciplines
Grammatical trigrams : a probabilistic model of link grammar by John Lafferty ( Book )
2 editions published in 1992 in English and held by 2 WorldCat member libraries worldwide
In this paper we present a new class of language models. This class derives from link grammar a context-free formalism for the description of natural language. We describe an algorithm for determining maximum-likelihood estimates of the parameters of these models. The language models which we present differ from previous models based on stochastic context-free grammars in that they are highly lexical. In particular they include the familiar n-gram models as a natural subclass The motivation for considering this class is to estimate the contribution which grammar can make to reducing the relative entropy of natural language
Duality and Auxiliary Functions for Bregman Distances (revised) by Stephen Della Pietra ( )
2 editions published between 2001 and 2002 in English and held by 2 WorldCat member libraries worldwide
In this paper, the authors formulate and prove a convex duality theorem for minimizing a general class of Bregman distances subject to linear constraints. The duality result is then used to derive iterative algorithms for solving the associated optimization problem. Their presentation is motivated by the recent work of Collins, Schapire, and Singer (2001), who showed how certain boosting algorithms and maximum likelihood logistic regression can be unified within the framework of Bregman distances. In particular, specific instances of the results given here are used by Collins et al. (2001) to show the convergence of a family of iterative algorithms for minimizing the exponential or logistic loss. Following an introduction, Section 2 recalls the standard definitions from convex analysis that will be required, and presents the technical assumptions made on the class of Bregman distances that the authors work with. They also introduce some new terminology, using the terms Legendre-Bregman conjugate and Legendre-Bregman projection to extend the classical notion of the Legendre conjugate and transform to Bregman distances. Section 3 contains the statement and proof of the duality theorem that connects the primal problem with its dual, showing that the solution is characterized in geometrical terms by a Pythagorean equality. Section 4 defines the notion of an auxiliary function, which is used to construct iterative algorithms for solving constrained optimization problems. This section shows how convexity can be used to derive an auxiliary function for Bregman distances based on separable functions. The last section summarizes the main results of the paper
Diffusion Kernels on Statistical Manifolds by John Lafferty ( Book )
2 editions published in 2004 in English and held by 2 WorldCat member libraries worldwide
A family of kernels for statistical learning is introduced that exploits the geometric structure of statistical models. The kernels are based on the heat equation on the Riemannian manifold defined by the Fisher information metric associated with a statistical family, and generalize the Gaussian kernel of Euclidean space. As an important special case, kernels based on the geometry of multinomial families are derived, leading to kernel-based learning algorithms that apply naturally to discrete data. Bounds on covering numbers and Rademacher averages for the kernels are proved using bounds on the eigenvalues of the Laplacian on Riemannian manifolds. Experimental results are presented form document classification, for which the use of multinomial geometry is natural and well motivated, and improvements are obtained over the standard use of Gaussian or linear kernels, which have been the standard for text classification
Time-sensitive Dirichlet process mixture models by Xiaojin Zhu ( Book )
2 editions published in 2005 in English and held by 2 WorldCat member libraries worldwide
We introduce Time-Sensitive Dirichlet Process Mixture models for clustering. The models allow infinite mixture components just like standard Dirichlet process mixture models. However, they also have the ability to model time correlations between instances
Level Spacings for SL(2,p) by John D Lafferty ( Book )
1 edition published in 1997 in English and held by 2 WorldCat member libraries worldwide
We investigate the eigenvalue spacing distributions for randomly generated 4-regular Cayley graphs on SL2(Fp) by numerically calculating their spectra. We present strong evidence that the distributions are Poisson and hence do not follow the Gaussian orthogonal ensemble. Among the Cayley graphs of SL2(Fp) we consider are the new expander graphs recently discovered by Y. Shalom. In addition, we use a Markov chain method to generate random 4-regular graphs and observe that the average eigenvalue spacings are closely approximated by the Wigner surmise
A Model of Lexical Attraction and Repulsion ( )
1 edition published in 1997 in Undetermined and held by 1 WorldCat member library worldwide
Variational inference and learning for a unified model of syntax, semantics and morphology by Leonid Kontorovich ( Book )
1 edition published in 2006 in English and held by 1 WorldCat member library worldwide
Abstract: "There have been recent attempts to produce trainable (unsupervised) models of human-language syntax and semantics, as well as morphology. To our knowledge, there has not been an attempt to produce a generative model that encorporates [sic] semantic, syntactic, and morphological elements. Some immediate applications of this tool are stemming, work clustering by root, and disambiguation (at the syntactic, semantic, and morphological levels). In this work, we propose a hierarchical topics-syntax-morphology model. We provide the variational inference and update rules for this model (exact inference is intractable). We show some preliminary results on segmentation tasks."
Semi-supervised learning : from Gaussian fields to Gaussian processes by Xiaojin Zhu ( Book )
1 edition published in 2003 in English and held by 1 WorldCat member library worldwide
Abstract: "We show that the Gaussian random fields and harmonic energy minimizing function framework for semi-supervised learning can be viewed in terms of Gaussian processes, with covariance matrices derived from the graph Laplacian. We derive hyperparameter learning with evidence maximization, and give an empirical study of various ways to parameterize the graph weights."
Advances in neural information processing systems. proceedings of the 2010 conference ( Book )
1 edition published in 2011 in English and held by 1 WorldCat member library worldwide
Gallipoli : battlefield tour 2007 by John Lafferty ( Book )
1 edition published in 2007 in English and held by 1 WorldCat member library worldwide
Kernel conditional random fields : representation, clique selection, and semi-supervised learning by John Lafferty ( Book )
1 edition published in 2004 in English and held by 1 WorldCat member library worldwide
Abstract: "Kernel conditional random fields are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The clique selection and semi-supervised methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction."
 
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English (43)
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