Lecture
Applying the learning bound to kernel regression
In course
DEMO: magna deserunt
Amet labore labore deserunt duis commodo nulla culpa nostrud occaecat sit consequat labore ad. Ea ullamco aute aliquip id ea veniam laboris. Eu consectetur voluptate et et fugiat cillum duis velit incididunt consequat veniam enim commodo ea. Qui exercitation esse tempor mollit labore in quis minim. Commodo deserunt anim velit commodo amet in. Ex amet labore culpa pariatur incididunt commodo in tempor ullamco.
Description
This lecture applies the main theorem of the course to the case of least square regression in a Reproducing Kernel Hilbert Space (RKHS), discussing the application to LR of the Rademacher bound and the Lipschitz constant of the mapping. The slides cover various aspects of the application, including the family of functions, probability considerations, and the risk associated with the regression. The lecture delves into the complexity of the functions, the interval of values, and the rate of convergence, emphasizing the key concepts of the learning bound and kernel regression.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (53)
Bayesian Inference: Estimation & Demystification
Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.
Linear Regression: Ozone Data Analysis
Explores linear regression analysis of ozone data using statistical models.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Linear Regression: Estimation and Testing
Explores linear regression estimation, hypothesis testing, and practical applications in statistics.
Modeling Elasticity and Coefficients
Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.