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Results 1 - 10
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64,571
An Empirical Study of Smoothing Techniques for Language Modeling
, 1998
"... We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e.g., Br ..."
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Cited by 1224 (21 self)
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We present an extensive empirical comparison of several smoothing techniques in the domain of language modeling, including those described by Jelinek and Mercer (1980), Katz (1987), and Church and Gale (1991). We investigate for the first time how factors such as training data size, corpus (e
Determinants of Economic Growth: A Cross-Country Empirical Study
, 1996
"... Empirical findings for a panel of around 100 countries from 1960 to 1990 strongly support the general notion of conditional convergence. For a given starting level of real per capita GDP, the growth rate is enhanced by higher initial schooling and life expectancy, lower fertility, lower government c ..."
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Cited by 892 (12 self)
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Empirical findings for a panel of around 100 countries from 1960 to 1990 strongly support the general notion of conditional convergence. For a given starting level of real per capita GDP, the growth rate is enhanced by higher initial schooling and life expectancy, lower fertility, lower government
Loopy belief propagation for approximate inference: An empirical study. In:
- Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract
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Cited by 676 (15 self)
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to work well. In this paper we investigate loopy prop agation empirically under a wider range of conditions. Is there something special about the error-correcting code setting, or does loopy propagation work as an approximation scheme for a wider range of networks? ..\ x(:x).) (1) where: and: The message
An extensive empirical study of feature selection metrics for text classification
- J. of Machine Learning Research
, 2003
"... Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison ..."
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Cited by 496 (15 self)
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Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison
An Empirical Study of Operating System Errors
, 2001
"... We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previ-ous studies that consider errors found by manual inspec-tion of logs, testing, and surveys because static analysis is applied uniforml ..."
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Cited by 363 (9 self)
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We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previ-ous studies that consider errors found by manual inspec-tion of logs, testing, and surveys because static analysis is applied
The Strength of Weak Ties: A Network Theory Revisited
- Sociological Theory
, 1982
"... In this chapter I review empirical studies directly testing the ..."
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Cited by 903 (2 self)
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In this chapter I review empirical studies directly testing the
Popular ensemble methods: an empirical study
- Journal of Artificial Intelligence Research
, 1999
"... An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Baggi ..."
Abstract
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Cited by 296 (4 self)
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. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work
An Empirical Study of the Reliability of UNIX Utilities
- In Proceedings of the Workshop of Parallel and Distributed Debugging
, 1990
"... This report describes these tests and an analysis of the program bugs that caused the crashes. Content Indicators D.2.5 (Testing and Debugging), D.4.9 (Programs and Utilities), General term: reliability, UNIX. #################################### ..."
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Cited by 292 (5 self)
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This report describes these tests and an analysis of the program bugs that caused the crashes. Content Indicators D.2.5 (Testing and Debugging), D.4.9 (Programs and Utilities), General term: reliability, UNIX. ####################################
An empirical study of learning speed in back-propagation networks
, 1988
"... Most connectionist or "neural network" learning systems use some form of the back-propagation algorithm. However, back-propagation learning is too slow for many applications, and it scales up poorly as tasks become larger and more complex. The factors governing learning speed are poorly un ..."
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Cited by 278 (0 self)
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understood. I have begun a systematic, empirical study of learning speed in backprop-like algorithms, measured against a variety of benchmark problems. The goal is twofold: to develop faster learning algorithms and to contribute to the development of a methodology that will be of value in future studies
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.
- Machine Learning,
, 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
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Cited by 707 (2 self)
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Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several
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64,571
