Lazy Learning Meets the Recursive Least Squares Algorithm

By Mauro Birattari, Gianluca Bontempi, Hugues Bersini. 1998

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Souhaib Ben Taieb
2. Gianluca Bontempi
3. Amir F. Atiya
4. Yinjun Wu
5. Edgar Dobriban
6. Antonio A. Ginart
7. Susan B. Davidson
8. James Y. Zou

Citations in the corpus

• DeltaGrad: Rapid retraining of machine learning models (2020, methodology): “Effcient model updating for adding and removing data-points is possible for linear models, based on effcient rank one updates of matrix inverses (e.g., Birattari et al., 1999; Horn & Johnson, 2012; Cao & Yang, 2015, etc).”
• Making AI Forget You: Data Deletion in Machine Learning (2019, methodology): “Laziness Lazy learning methods delay computation until inference time [88, 11, 6], resulting in trivial deletions.”
• A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition (2011, methodology): “Another approach is the divide-and-conquer modeling which consists in relaxing the global modeling assumptions by dividing a complex problem into simpler problems, whose solutions can be combined to yield a solution to the original problem (Bontempi, 1999).”