Lecture:2019/6/27-2814:00-16:00, Prof. Lin Wenwei

Date:2019-06-27Views:63

Title:Nonlinear Smooth Support Vector Machines I, II

Nonlinear Smooth Support Vector Machines,

  

Time:6/27, 14:00-16:00)

Abstract:

(1) Review of optimization problems with constraints

----Primal form, dual form, Karush-Kuhn-Tuker (KKT) conditions.

----Tangent vectors to feasible set and linearized feasible directions.

  

(2) Binary classification problems/Supervised learning problems

----Linearly separable case: Maximizing the margin between boundary planes, primal and dualforms.

----Nonseparable case: primal/dual maximization problems for 1-norm/2-norm soft margin SVM.

  

(3) Nonlinear support vector machine

----Two spiral data set.

----Learning linear machine in feature space.

----Kernel: represent inner product in feature space.

----Kernel Techniques: monomials of degree d, polynomial kernel, Guassian (radial basis function) kernel.

----Dual representation of SVM classifier.

  

(4) Smooth support vector machine

----SVM as an unconstrained minimization problem.

----Smooth with plus function.

----Newton-Armijo Algorithm.

  

(5) Nonlinear smooth support vector machine

----Nonlinear SSVM motivation.

----Kernel trick: Gaussian kernel, monomials, polynomials.

----Nonlinear classifier.

  

(6) Reduced support vector machine

----Reduced SVM: A compressed model.

----A nonlinear kernel application: checkerboard training set.

----Using 50 randomly selected points out of 1000 points.

----Compressed model vs full model.

  

  

  

  

  

  

  

  

   

  

Title:Clustering and Expectation/Maximization Algorithms I, II

    

(Time6/28, 14:00-16:00)

  

Abstract:

(1) Searching the optimal combination of the regularization parameter and the width parameter in the Gaussian kernel

----Grid search, Nested uniform design method (UDM).

----Experimental results: grid search vs UDM (13/9) vs UDM(9/5).

  

(2) Three fundamental algorithms

----Naive Bayes classifier.

----K-nearest neighbors algorithm.

----Online perception algorithm.

  

(3) Unsupervised learning problems

----K-Means clustering problem formulation.

----K-Means Algorithm.

----K-Means ++ Algorithm.

  

(4) Expectation/Maximization Algorithm

----E-step: Compute the probability, the point n is generated by distribution k.

----M-step: update mean, variance and probability distribution k.


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