منابع مشابه
Sample size planning for classification models.
In biospectroscopy, suitably annotated and statistically independent samples (e.g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classifiers. However, building a good model is actually not enough: the performance ...
متن کاملSample Size Planning 1 Running Head : Sample Size Planning Sample Size Planning with Effect Size Estimates
The use of effect size estimates in planning the sample size necessary for a future study can introduce substantial bias in the sample size planning process. For instance, the uncertainty associated with the effect size estimate may result in average statistical power that is substantially lower than the nominal power specified in the calculation. The present manuscript examines methods for inc...
متن کاملSample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملBayesian Sample size Determination for Longitudinal Studies with Continuous Response using Marginal Models
Introduction Longitudinal study designs are common in a lot of scientific researches, especially in medical, social and economic sciences. The reason is that longitudinal studies allow researchers to measure changes of each individual over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. A st...
متن کاملPredicting sample size required for classification performance
BACKGROUND Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. METHODS We designed and implemented a method that fits an inv...
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ژورنال
عنوان ژورنال: Analytica Chimica Acta
سال: 2013
ISSN: 0003-2670
DOI: 10.1016/j.aca.2012.11.007