• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 2,809,303
Next 10 →

A Systematic Comparison of Various Statistical Alignment Models

by Franz Josef Och, Hermann Ney - COMPUTATIONAL LINGUISTICS , 2003
"... ..."
Abstract - Cited by 1831 (70 self) - Add to MetaCart
Abstract not found

Groupware: Some issues and experiences

by C. A. Ellis, S. J. Gibbs, G.L. Rein - COMMUNICATIONS OF THE ACM , 1991
"... ..."
Abstract - Cited by 910 (2 self) - Add to MetaCart
Abstract not found

Experiments with a New Boosting Algorithm

by Yoav Freund, Robert E. Schapire , 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
Abstract - Cited by 2176 (21 self) - Add to MetaCart
learning problems. We performed two sets of experiments. The first set compared boosting to Breiman’s “bagging ” method when used to aggregate various classifiers (including decision trees and single attribute-value tests). We compared the performance of the two methods on a collection of machine

Distributed Computing in Practice: The Condor Experience

by Douglas Thain, Todd Tannenbaum, Miron Livny - Concurrency and Computation: Practice and Experience , 2005
"... Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational grid. In this chapter, we provide the history ..."
Abstract - Cited by 542 (7 self) - Add to MetaCart
, we reflect on the lessons of experience and chart the course traveled by research ideas as they grow into production systems.

Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms

by Michael Collins , 2002
"... We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modific ..."
Abstract - Cited by 641 (16 self) - Add to MetaCart
We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modification of the proof of convergence of the perceptron algorithm for classification problems. We give experimental results on part-of-speech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximum-entropy tagger.

Z-Tree: Zurich Toolbox for Readymade Economic Experiments, Working paper No

by Urs Fischbacher , 1999
"... 2.2.2 Start-up of the Experimenter PC............................................................................................... 9 2.2.3 Start-up of the Subject PCs....................................................................................................... 9 ..."
Abstract - Cited by 1956 (33 self) - Add to MetaCart
2.2.2 Start-up of the Experimenter PC............................................................................................... 9 2.2.3 Start-up of the Subject PCs....................................................................................................... 9

Empirical Bayes Analysis of a Microarray Experiment

by Bradley Efron, Robert Tibshirani, John D. Storey, Virginia Tusher - Journal of the American Statistical Association , 2001
"... Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment in whi ..."
Abstract - Cited by 488 (19 self) - Add to MetaCart
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raising serious problems of data reduction, and simultaneous inference. We consider one such experiment

On the algorithmic implementation of multi-class kernel-based vector machines

by Koby Crammer, Yoram Singer, Nello Cristianini, John Shawe-taylor, Bob Williamson - Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernel-based vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
Abstract - Cited by 547 (14 self) - Add to MetaCart
significant running time improvements for large datasets. Finally, we describe various experiments with our approach comparing it to previously studied kernel-based methods. Our experiments indicate that for multiclass problems we attain state-of-the-art accuracy.

A Meta-Analytic Review of Experiments Examining the Effects of Extrinsic Rewards on Intrinsic Motivation

by Edward L. Deci, Richard Koestner, Richard M. Ryan
"... A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards signifi-cantly undermined free-choice intrinsic motivation (d =-0.40,-0.36, and-0.28, respectively), as did ..."
Abstract - Cited by 602 (16 self) - Add to MetaCart
A meta-analysis of 128 studies examined the effects of extrinsic rewards on intrinsic motivation. As predicted, engagement-contingent, completion-contingent, and performance-contingent rewards signifi-cantly undermined free-choice intrinsic motivation (d =-0.40,-0.36, and-0.28, respectively), as did all rewards, all tangible rewards, and all expected rewards. Engagement-contingent and completion-contingent rewards also significantly undermined self-reported interest (d =-0.15, and —0.17), as did all tangible rewards and all expected rewards. Positive feedback enhanced both free-choice behavior (d = 0.33) and self-reported interest (d = 0.31). Tangible rewards tended to be more detrimental for children than college students, and verbal rewards tended to be less enhancing for children than college students. The authors review 4 previous meta-analyses of this literature and detail how this study's methods, analyses, and results differed from the previous ones. By 1971, hundreds of studies within the operant tradition (Skin-ner, 1953) had established that extrinsic rewards can control be-havior. When administered closely subsequent to a behavior, re-wards were reliably found to increase the likelihood that the behavior would be emitted again, an effect that persisted as long as

Improved Statistical Alignment Models

by Franz Josef Och, Hermann Ney - In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics , 2000
"... In this paper, we present and compare various single-word based alignment models for statistical machine translation. We discuss the five IBM alignment models, the Hidden-Markov alignment model, smoothing techniques and various modifications. ..."
Abstract - Cited by 593 (13 self) - Add to MetaCart
In this paper, we present and compare various single-word based alignment models for statistical machine translation. We discuss the five IBM alignment models, the Hidden-Markov alignment model, smoothing techniques and various modifications.
Next 10 →
Results 1 - 10 of 2,809,303
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University