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with model uncertainty on small data sets.
"... Abstract: Datasets of population dynamics are typically characterized by a short temporal extension. In this condition, several alternative models typically achieve close accuracy, though returning quite different predictions (model uncertainty). Bayesian model averaging (BMA) addresses this issue b ..."
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by averaging the prediction of the different models, using as weights the posterior probability of the models. However, an open problem of BMA is the choice of the prior probability of the models, which can largely impact on the inferences, especially when data are scarce. We present Credal Model Averaging
Muoz Entropy estimates of small data sets
 J. Phys. A Math. Theor
, 2008
"... Abstract. Estimating entropies from limited data series is known to be a nontrivial task. Naïve estimations are plagued with both systematic (bias) and statistical errors. Here, we present a new “balanced estimator ” for entropy functionals (Shannon, Rényi and Tsallis) specially devised to provide ..."
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Cited by 15 (0 self)
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a compromise between low bias and small statistical errors, for short data series. This new estimator outperforms other currently available ones when the data sets are small and the probabilities of the possible outputs of the random variable are not close to zero. Otherwise, other well
Generalization And Maximum Likelihood From Small Data Sets
 Proc. IEEESP Workshop on Neural Networks for Signal Processing
, 1993
"... INTRODUCTION An often encountered learning problem is maximum likelihood training of exponential models. When the state is only partially specified by the training data, iterative training algorithms are used to produce a sequence of models that assign increasing likelihood to the training data. Al ..."
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Cited by 8 (6 self)
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. Although the performance as measured on the training set continues to improve as the algorithms progress, performance on related data sets may eventually begin to deteriorate. The cause of this behavior can be seen when the training problem is stated in the Alternating Minimization framework [1]. A
Bagging Is A SmallDataSet Phenomenon
 IN INTERNATIONAL CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR
, 2001
"... Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, the use of disjoint par ..."
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Cited by 8 (3 self)
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Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, the use of disjoint
On Small Data Sets Revealing Big Differences
 Proceedings of the 4th Panhellenic conference on Artificial Intelligence, Heraklion, Greece, Springer LNCS 3955
, 2006
"... Abstract. We use decision trees and genetic algorithms to analyze the academic performance of students throughout an academic year at a distance learning university. Based on the accuracy of the generated rules, and on crossexaminations of various groups of the same student population, we surprising ..."
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Cited by 3 (1 self)
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Abstract. We use decision trees and genetic algorithms to analyze the academic performance of students throughout an academic year at a distance learning university. Based on the accuracy of the generated rules, and on crossexaminations of various groups of the same student population, we surprisingly observe that students ’ performance is clustered around tutors. 1
• Second session: Experiments – Experiments in small data settings
, 2006
"... • Open source toolkit – advances stateoftheart of statistical machine translation models – best performance of European Parliament task – competitive on IWSLT and TCStar • Factored models – outperform traditional phrasebased models – framework for a wide range of models – integrated approach to ..."
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• Open source toolkit – advances stateoftheart of statistical machine translation models – best performance of European Parliament task – competitive on IWSLT and TCStar • Factored models – outperform traditional phrasebased models – framework for a wide range of models – integrated approach to morphology and syntax • Confusion networks – exploit ambiguous input and outperform 1best – enable integrated approach to speech translation
A practical method for calculating largest Lyapunov exponents from small data sets
 PHYSICA D
, 1993
"... Detecting the presence of chaos in a dynamical system is an important problem that is solved by measuring the largest Lyapunov exponent. Lyapunov exponents quantify the exponential divergence of initially close statespace trajectories and estimate the amount of chaos in a system. We present a new m ..."
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Cited by 166 (0 self)
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to changes in the following quantities: embedding dimension, size of data set, reconstruction delay, and noise level. Furthermore, one may use the algorithm to calculate simultaneously the correlation dimension. Thus, one sequence of computations will yield an estimate of both the level of chaos
Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation
"... We address the problem of improving the reliability of independencebased causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence ..."
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Cited by 5 (3 self)
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We address the problem of improving the reliability of independencebased causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence
Practice of Epidemiology Correcting for Optimistic Prediction in Small Data Sets
, 2013
"... The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use suchmethods, and ..."
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The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use suchmethods
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