Introduction to Radial Basis Function - PowerPoint PPT Presentation

About This Presentation
Title:

Introduction to Radial Basis Function

Description:

monotonically decreases with distance from center. monotonically increases with distance from center. Gaussian RBF. multiqradric RBF. Least Squares ... – PowerPoint PPT presentation

Number of Views:85
Avg rating:3.0/5.0
Slides: 25
Provided by: csie
Learn more at: https://csie.org
Category:

less

Transcript and Presenter's Notes

Title: Introduction to Radial Basis Function


1
Introduction to Radial Basis Function
  • Mark J. L. Orr

2
Radial Basis Function Networks
  • Linear model

3
Radial functions
  • Gassian RBFc center, r radius
  • monotonically decreases with distance from center
  • Multiquadric RBF
  • monotonically increases with distance from center

4
Gaussian RBF
multiqradric RBF
5
Least Squares
  • model
  • training data (x1, y1), (x2, y2), , (xp, yp)
  • minimize the sum-squared-error

6
Example
  • Sample points (noisy) from the curve y x
    (1, 1.1), (2, 1.8), (3, 3.1)
  • linear model f(x) w1h1(x) w2h2(x),where
    h1(x) 1, h2(x) x
  • estimate the coefficient w1, w2

7
  • f(x) x

8
  • New model f(x) w1h1(x) w2h2(x)
    w3h3(x)where h1(x) 1, h2(x) x, h3(x) x2

9
  • absorb all the noise overfit
  • If the model is too flexible, it will fit the
    noise
  • If it is too inflexible, it will miss the target

10
The optimal weight vector
  • model
  • sum-squared-error
  • cost function weight penalty term is added

11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
Example
  • Sample points (noisy) from the curve y x
    (1, 1.1), (2, 1.8), (3, 3.1)
  • linear model f(x) w1h1(x) w2h2(x),where
    h1(x) 1, h2(x) x
  • estimate the coefficient w1, w2

16
(No Transcript)
17
The projection matrix
  • At the optimal weightthe value of cost function
    C yTPythe sum-squared-error S yTP2y

18
Model selection criteria
  • estimates of how well the trained model will
    perform on future input
  • standard tool cross validation
  • error variance

19
Cross validation
  • leave-one-out (LOO) cross-validation
  • generalized cross-validation

20
Ridge regression
  • mean-squared-error

21
Global ridge regression
  • Use GCV
  • re-estimation formula
  • initialize ?
  • re-estimate ?, until convergence

22
Local ridge regression
  • research problem

23
Example
24
Selection the RBF
  • forward selection
  • starts with an empty subset
  • added one basis function at a time
  • most reduces the sum-squared-error
  • until some chosen criterion stops
  • backward elimination
  • starts with the full subset
  • removed one basis function at a time
  • least increases the sum-squared-error
  • until the chosen criterion stops decreasing
Write a Comment
User Comments (0)
About PowerShow.com