Results 271 to 280 of about 10,630,553 (293)
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A Bayesian Independence Test for Small Datasets
IEEE Transactions on Signal Processing, 2006We propose a Bayesian test for independence among signals where only a small dataset is available. Traditional frequentist approaches often fail in this case due to inaccurate estimation of either the source statistical models or the threshold used by the test statistics.
Chin-Jen Ku, Terrence L. Fine
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Meta Learning on Small Biomedical Datasets
2016Meta-learning is one of subsections of supervised machine learning that has continuously grown with interests to apply on new data sets in the late years. Meta learning is the process of knowledge that is acquired by the examples. Bagging, dagging, decorate, rotation forest, and filtered classifiers are well known meta-learning algorithms that are ...
Ibrikci T. +2 more
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A Neural-based Architecture For Small Datasets Classification
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020Digital Libraries benefit from the use of text classification strategies since they are enablers for performing many document management tasks like Information Retrieval. The effectiveness of such classification strategies depends on the amount of available data and the classifier used.
Andi Rexha, Mauro Dragoni, Roman Kern
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Analysing extremely small sized ratio datasets
International Journal of Bioinformatics Research and Applications, 2015The naïve use of expression ratios in high-throughput biological studies can greatly limit analytical outcome especially when sample size is small. In the worst-case scenario, with only one reference and one test state each (often due to the severe lack of study material); such limitations make it difficult to perform statistically meaningful analysis.
Piero Ricchiuto +2 more
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Conditional GAN for Small Datasets
2022 IEEE International Symposium on Multimedia (ISM), 2022Komei Hiruta +4 more
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The importance of hyperparameters selection within small datasets
2015 International Joint Conference on Neural Networks (IJCNN), 2015Gaussian Process is a Machine Learning technique that has been applied to the analysis of percutaneous absorption of chemicals through human skin. The normal, automatic method of setting the hyperparameters associated with Gaussian Processes may not be suitable for small datasets.
Parivash Ashrafi +6 more
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