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
(time：6/28, 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.
