Results 11 - 20 of 100,264

Inverse entailment and Progol

by Stephen Muggleton , 1995
"... This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol ..."
Abstract - Cited by 719 (61 self) - Add to MetaCart
This paper firstly provides a re-appraisal of the development of techniques for inverting deduction, secondly introduces Mode-Directed Inverse Entailment (MDIE) as a generalisation and enhancement of previous approaches and thirdly describes an implementation of MDIE in the Progol system. Progol

Literate programming

by Donald E. Knuth - THE COMPUTER JOURNAL , 1984
"... The author and his associates have been experimenting for the past several years with a programming language and documentation system called WEB. This paper presents WEB by example, and discusses why the new system appears to be an improvement over previous ones. ..."
Abstract - Cited by 557 (3 self) - Add to MetaCart
The author and his associates have been experimenting for the past several years with a programming language and documentation system called WEB. This paper presents WEB by example, and discusses why the new system appears to be an improvement over previous ones.

previous paper (Garcia Rodriguez LA,

by A Drug–drug Interaction, Luis Alberto, García Rodríguez, Karine Egan, Garret A. Fitzgerald, Varas-lorenzo C, Maguire A, Perez A Nonsteroidal, Via Pharmaceuticals , 2006
"... no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ..."
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no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Practical Byzantine fault tolerance

by Miguel Castro, Barbara Liskov , 1999
"... This paper describes a new replication algorithm that is able to tolerate Byzantine faults. We believe that Byzantinefault-tolerant algorithms will be increasingly important in the future because malicious attacks and software errors are increasingly common and can cause faulty nodes to exhibit arbi ..."
Abstract - Cited by 673 (15 self) - Add to MetaCart
arbitrary behavior. Whereas previous algorithms assumed a synchronous system or were too slow to be used in practice, the algorithm described in this paper is practical: it works in asynchronous environments like the Internet and incorporates several important optimizations that improve the response time

Critical values for cointegration tests

by James G. Mackinnon, James G. Mackinnon - Eds.), Long-Run Economic Relationship: Readings in Cointegration , 1991
"... This paper provides tables of critical values for some popular tests of cointegration and unit roots. Although these tables are necessarily based on computer simulations, they are much more accurate than those previously available. The results of the simulation experiments are summarized by means of ..."
Abstract - Cited by 506 (3 self) - Add to MetaCart
This paper provides tables of critical values for some popular tests of cointegration and unit roots. Although these tables are necessarily based on computer simulations, they are much more accurate than those previously available. The results of the simulation experiments are summarized by means

A Bayesian method for the induction of probabilistic networks from data

by Gregory F. Cooper, EDWARD HERSKOVITS - MACHINE LEARNING , 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract - Cited by 1400 (31 self) - Add to MetaCart
network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.

A Maximum Entropy Model for Part-Of-Speech Tagging

by Adwait Ratnaparkhi , 1996
"... This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" t ..."
Abstract - Cited by 580 (1 self) - Add to MetaCart
This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "

The geometry of algorithms with orthogonality constraints

by Alan Edelman, Tomás A. Arias, Steven T. Smith - SIAM J. MATRIX ANAL. APPL , 1998
"... In this paper we develop new Newton and conjugate gradient algorithms on the Grassmann and Stiefel manifolds. These manifolds represent the constraints that arise in such areas as the symmetric eigenvalue problem, nonlinear eigenvalue problems, electronic structures computations, and signal proces ..."
Abstract - Cited by 640 (1 self) - Add to MetaCart
of previously unrelated algorithms. It is our hope that developers of new algorithms and perturbation theories will benefit from the theory, methods, and examples in this paper.

Coarse-to-fine n-best parsing and MaxEnt discriminative reranking

by Eugene Charniak, Mark Johnson - In ACL , 2005
"... Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a co ..."
Abstract - Cited by 522 (15 self) - Add to MetaCart
Discriminative reranking is one method for constructing high-performance statistical parsers (Collins, 2000). A discriminative reranker requires a source of candidate parses for each sentence. This paper describes a simple yet novel method for constructing sets of 50-best parses based on a

Ensemble Methods in Machine Learning

by Thomas G. Dietterich - MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857 , 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract - Cited by 625 (3 self) - Add to MetaCart
, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Results 11 - 20 of 100,264