-
SVM Solution in the Span of the Data
This blog explores how the span property simplifies optimization in SVM and ridge regression, introduces the Representer Theorem, and highlights the computational benefits of kernelization.
-
Understanding the Kernel Trick
A step-by-step exploration of kernel methods, unraveling their role in enabling powerful nonlinear modeling through the elegance of the kernel trick.
-
Unleashing the Power of Linear Models - Tackling Nonlinearity with Feature Maps
Explore how feature maps transform inputs, handle nonlinearities, and expand the expressiveness of linear models with practical examples and intuitive solutions.
-
Demystifying SVMs - Understanding Complementary Slackness and Support Vectors
A deep dive into the complementary slackness conditions in SVMs, exploring their connection to margins, support vectors, and kernelized optimization for powerful classification.
-
The Dual Problem of SVM
An in-depth exploration of the dual problem in SVMs, covering its mathematical foundation, Lagrangian formulation, duality principles, and complementary slackness for intuitive understanding.