Results 31 to 40 of about 1,243,089 (305)
British Journal of Sports Medicine, 2004 This article reviews the history, technique, effects, side effects, and detection of blood boosting. It also considers whether or not this particular performance enhancement technique is a thing of the past or a continuing form of abuse among athletes.
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Interpreting uninterpretable predictors: kernel methods, Shtarkov solutions, and random forests
Statistical Theory and Related Fields, 2022 Many of the best predictors for complex problems are typically regarded as hard to interpret physically. These include kernel methods, Shtarkov solutions, and random forests.
T. M. Le, Bertrand Clarke
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Boosting Techniques for Nonlinear Time Series Models [PDF]
, 2010 Many of the popular nonlinear time series models require a priori the choice of parametric functions which are assumed to be appropriate in specific applications.
Hothorn, Torsten +2 more
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Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals
IEEE Access, 2019 The application of wireless sensors in the brain-computer interface (BCI) system provides great convenience for the acquisition of electroencephalography (EEG) signals.
Mengxi Dai +4 more
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Gradient boosting machines, a tutorial
Front. Neurorobot., 2013 Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications.
Alexey Natekin, A. Knoll
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Boosting methods for multi-class imbalanced data classification: an experimental review
Journal of Big Data, 2020 Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datasets.
J. Tanha +4 more
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Feature Weighting and Boosting for Few-Shot Segmentation [PDF]
IEEE International Conference on Computer Vision, 2019 This paper is about few-shot segmentation of foreground objects in images. We train a CNN on small subsets of training images, each mimicking the few-shot setting.
Khoi Duc Minh Nguyen, S. Todorovic
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Machine Learning-Based Forecasting of Bitcoin Price Movements
Proceedings of the International Conference on Applied Innovations in ITIn the volatile realm of cryptocurrency markets, this research explores the intricate dance of Bitcoin price dynamics through the lens of machine learning. Employing a multifaceted approach, we harness the power of Long Short-Term Memory (LSTM) networks,
Darko Angelovski +4 more
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Binary and Ordinal Random Effects Models Including Variable Selection [PDF]
, 2010 A likelihood-based boosting approach for fitting binary and ordinal mixed models is presented. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available.
Groll, Andreas, Tutz, Gerhard
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PLoS ONE, 2013 Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on
Schmid, Matthias +5 more
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