Results 1 - 10 of 239,362

The structure and function of complex networks

by M. E. J. Newman - SIAM REVIEW , 2003
"... Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, ..."
Abstract - Cited by 2600 (7 self) - Add to MetaCart
Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field

The Infinite Hidden Markov Model

by Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - Machine Learning , 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
Abstract - Cited by 637 (41 self) - Add to MetaCart
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data

Experimental Estimates of Education Production Functions

by Alan B. Krueger - Princeton University, Industrial Relations Section Working Paper No. 379 , 1997
"... This paper analyzes data on 11,600 students and their teachers who were randomly assigned to different size classes from kindergarten through third grade. Statistical methods are used to adjust for nonrandom attrition and transitions between classes. The main conclusions are (1) on average, performa ..."
Abstract - Cited by 529 (19 self) - Add to MetaCart
This paper analyzes data on 11,600 students and their teachers who were randomly assigned to different size classes from kindergarten through third grade. Statistical methods are used to adjust for nonrandom attrition and transitions between classes. The main conclusions are (1) on average

A model for technical inefficiency effects in a stochastic frontier production function for panel data

by G. E. Battese - Empirical Economics , 1995
"... Abstract: A stochastic frontier production function is defined for panel data on firms, in which the non-negative technical inetGciency effects are assumed to be a function of firm-specific variables and time. The inefficiency effects are assumed to be independently distributed as truncations of nor ..."
Abstract - Cited by 555 (4 self) - Add to MetaCart
of normal distributions with constant variance, but with means which are a linear function of observable variables. This panel data model is an extension of recently proposed models for inefTiciency effects in stochastic frontiers for cross-sectional data. An empirical application of the model is obtained

Model checking and abstraction

by Peter J. Clarke, Djuradj Babich, Tariq M. King, B. M. Golam Kibria - ACM Transactions on Programming Languages and Systems , 1994
"... software developers are using the Java language as the language of choice on many applications. This is due to the effective use of the object-oriented (OO) paradigm to develop large software projects and the ability of the Java language to support the increasing use of web technologies in business ..."
Abstract - Cited by 742 (55 self) - Add to MetaCart
applications. The recent release of the Java version 5.0 has further increased its popularity due to the inclusion of new features that exist in other OO languages. The transition from Java 1.4.x to Java 1.5.x has provided the programmer with more flexibility when implementing programs in Java. In this paper

Greedy Function Approximation: A Gradient Boosting Machine

by Jerome H. Friedman - Annals of Statistics , 2000
"... Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additi ..."
Abstract - Cited by 1000 (13 self) - Add to MetaCart
Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed

Bayesian Analysis of Stochastic Volatility Models

by Eric Jacquier, Nicholas G. Polson, Peter E. Rossi , 1994
"... this article is to develop new methods for inference and prediction in a simple class of stochastic volatility models in which logarithm of conditional volatility follows an autoregressive (AR) times series model. Unlike the autoregressive conditional heteroscedasticity (ARCH) and gener- alized ARCH ..."
Abstract - Cited by 601 (26 self) - Add to MetaCart
ARCH (GARCH) models [see Bollerslev, Chou, and Kroner (1992) for a survey of ARCH modeling], both the mean and log-volatility equations have separate error terms. The ease of evaluating the ARCH likelihood function and the ability of the ARCH specification to accommodate the timevarying volatility

Plenoptic Modeling: An Image-Based Rendering System

by Leonard McMillan, Gary Bishop , 1995
"... Image-based rendering is a powerful new approach for generating real-time photorealistic computer graphics. It can provide convincing animations without an explicit geometric representation. We use the “plenoptic function” of Adelson and Bergen to provide a concise problem statement for image-based ..."
Abstract - Cited by 760 (20 self) - Add to MetaCart
Image-based rendering is a powerful new approach for generating real-time photorealistic computer graphics. It can provide convincing animations without an explicit geometric representation. We use the “plenoptic function” of Adelson and Bergen to provide a concise problem statement for image

Hierarchical Models of Object Recognition in Cortex

by Maximilian Riesenhuber, Tomaso Poggio , 1999
"... The classical model of visual processing in cortex is a hierarchy of increasingly sophisticated representations, extending in a natural way the model of simple to complex cells of Hubel and Wiesel. Somewhat surprisingly, little quantitative modeling has been done in the last 15 years to explore th ..."
Abstract - Cited by 836 (84 self) - Add to MetaCart
predictions. The model is based on a novel MAX-like operation on the inputs to certain cortical neurons which may have a general role in cortical function.

SRILM -- An extensible language modeling toolkit

by Andreas Stolcke - IN PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING (ICSLP 2002 , 2002
"... SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation ..."
Abstract - Cited by 1218 (21 self) - Add to MetaCart
creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation
Results 1 - 10 of 239,362