1. Kernel Methods Jong Cheol Jeong

2. Out line 6.1 One-Dimensional Kernel Smoothers 6.1.1 Local Linear Regression 6.1.2 Local Polynomial Regression 6.2 Selecting the Width of Kernel 6.3 Local Regression in R p 6.4 Structured Local Regression Models in R p 6.5 Local Likelihood and Other Models 6.6 Kernel Density Estimation and Classification 6.7Radial Basis Functions and Kernels 6.8 Mixture Models for Density Estimation and Classification

3. Kernel Function: the kernel function is a weighting function by assigning weights to the nearby data points in making an estimate.

4. One-Dimensional Kernel Smoothers (6.1) K-nearest-neighbor

5. One-Dimensional Kernel Smoothers (6.2) Nadaraya-Watson kernel-weighted average With the Epanechnikov quadratic kernel (6.3) (6.4)

6. One-Dimensional Kernel Smoothers Adaptive neighborhoods with kernels (6.5) X [k] is the kth closest x i to x 0 (6.6) Tri-cube

7. One-Dimensional Kernel Smoothers Nearest-Neighbor kernel Vs. Epanechnikov kernel

8. Local Linear Regression

9. Locally weighted linear regression (6.7) Estimate function with equivalent kernel (6.8) (6.9)

10. Local Polynomial Regression Local quadratic regression (6.11) Trimming the hills and filling the valleys

11. Local Polynomial Regression Bias-variance tradeoff in selecting the polynomial degree

12. Selecting the Width of Kernel Bias-variance tradeoff in selecting the width The window is narrow then its variance will be relatively large, and the bias will tend to be small The window is wide then its variance will be relatively small, and the bias will tend to be higher

13. Local Regression in R p Local regression in p-dimension (6.12) D can be radial function or tri-cube function (6.13)

14. Structured Local Regression Models in R p Structured kernels (6.14) When the dimension to sample-size ratio is unfavorable, local regression does not help us much, unless we are willing to make some structural assumptions about the model - Downgrading or omitting coordinates can reduce the error Equation 6.13 gives equal weight to each coordinate, so we can modify the Kernel in order to control the weight on each coordinate

15. Structured Regression functions Fitting a regression function: considering every labels of interaction ANOVA decompositions: a statistical idea of analyzing the variances between different variables and find certain dependencies on subset of variables (6.15) Eliminating some of higher-order terms

16. Structured Regression functions Dividing the p predictors in X (6.16) Regression model by locally weighted least squares (6.17) Constructing a linear model for given Z Varying coefficient models Varying coefficient models: a special case of structured model

17. Questions Section 6.2 details how we may select the optimal lambda parameter for a kernel. How do we select the optimal kernel function? Are there kernels that tend to outperform others in most cases? If not, are there ways to determine a kernel that may perform well without doing an experiment?

18. Questions One benefit of using kernels with SVM's is that we can expand the dimensionality of the dataset and make it more likely to find a separating hyperplane with a hard margin. But section 6.3 says that for local regression, the proportion of points on the boundary increases to 1 as the dimensionality increases. Thus, the predictions we make will have even more bias. Is there a compromise solution that will work, or is the kernel trick best applied in classification problems?

19. Questions?