This article was automatically translated from the original Turkish version.
Advantage(s) | high generalization capability ideal for real-time applications Suitable for online learning | ||||||||
|---|---|---|---|---|---|---|---|---|---|
Year(Number) | 2011 | ||||||||
FTRL is an abbreviation for “Follow-The-Regularized-Leader” and is an optimization algorithm specifically designed for online learning scenarios. It was developed to work efficiently with large-scale and sparse data. This algorithm, proposed by Google, is widely used in ad prediction, recommendation systems, and real-time learning systems.
Unlike classical gradient descent methods, FTRL updates parameters based on a combination of accumulated gradients from previous steps and regularization terms. In particular, it incorporates
FTRL enables online and sparse learning by accounting for past gradients and regularization terms.
Bottou, Léon. 2012. “Stochastic Gradient Descent Tricks.” Lecture Notes in Computer Science, 421–36. https://doi.org/10.1007/978-3-642-35289-8_25.
McMahan, H. Brendan, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, et al. 2013. “Ad Click Prediction.” Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’13. https://doi.org/10.1145/2487575.2488200.
Advantage(s) | high generalization capability ideal for real-time applications Suitable for online learning | ||||||||
|---|---|---|---|---|---|---|---|---|---|
Year(Number) | 2011 | ||||||||
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FTRL Optimization Algorithm
Core Approach