About this document
Gradient Descent on Separable Data by laoshouxing is a document available to read on EtoBox.
The document presents a study on gradient descent applied to linearly separable data, detailing the conditions under which convergence occurs and the behavior of the algorithm over time. It establishes several theorems regarding the asymptotic properties of the weight vector and the loss function, demonstrating that the weight vector norm diverges while maintaining a specific directional behavior. The findings contribute to understanding the dynamics of optimization algorithms in machine learning contexts.
- Author
- laoshouxing
- Language
- EN