Download presentation

Presentation is loading. Please wait.

Published byHarmony Coxen Modified over 2 years ago

1
Christoph F. Eick Questions and Topics Review Nov. 30, 2010 1.Give an example of a problem that might benefit from feature creation 2.How does DENCLUE form clusters? Why does DENCLUE use grid-cells? What are the main differences between DENCLUE and DBSCAN? 3.Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (0,1), (2,2)} {(3,2), (3,3)}. 4.Compare Decision Trees, Support Vector Machines, and K-NN with respect to the number of decision boundary each approach uses! 5.K-NN is a lazy approach; what does it mean? What are the disadvantages of K-NN’s lazy approach? Do you see any advantages in using K-NN’s lazy approach. 6.Why do some support vector machine approaches map examples from a lower dimensional space to a higher dimensional space? 7.What is the role of slack variables in the Linear/SVM/Non-separable approach (textbook pages 266-270)—what do they measure? What properties of hyperplanes are maximized by the objective function f(w) (on page 268) in the approach? Silhouette: For an individual point, i –Calculate a = average distance of i to the points in its cluster –Calculate b = min (average distance of i to points in another cluster) –The silhouette coefficient for a point is then given by: s = (b-a)/max(a,b)

2
Christoph F. Eick Support Vector Machines What if the problem is not linearly separable?

3
Tan, Steinbach, Kumar, Eick: NN-Classifiers and Support Vector Machines Linear SVM for Non-linearly Separable Problems What if the problem is not linearly separable? –Introduce slack variables Need to minimize: Subject to (i=1,..,N): C is chosen using a validation set trying to keep the margins wide while keeping the training error low. Measures testing error Inverse size of margin between hyperplanes Parameter Slack variable allows constraint violation to a certain degree

4
Christoph F. Eick Questions and Topics Review Nov. 30, 2010 1.Discussion of Problem1/2of Assignment4 2.Give an example of a problem that might benefit from feature creation 3.How does DENCLUE form clusters? Why does DENCLUE use grid-cells? What are the main differences between DENCLUE and DBSCAN? 4.Compute the Silhouette of the following clustering that consists of 2 clusters: {(0,0), (0.1), (2,2)} {(3,2), (3,3)}. 6.Compare Decision Trees, Support Vector Machines, and K-NN with respect to the number of decision boundary each approach uses! DT: many, rectangular for numerical attributes K-NN: many, convex polygons (Voronoi cells), SVM: one, hyperplane 6.K-NN is a lazy approach; what does it mean? What are the disadvantages of K-NN’s lazy approach? Do you see any advantages in using K-NN’s lazy approach. … advantages: for quickly changing streaming data learning the model might be a waste of time and a lazy approach might be better… 7.Why do some support vector machine approaches map examples from a lower dimensional space to a higher dimensional space? To make them linearly separable. 7.What is the role of slack variables in the Linear/SVM/Non-separable approach (textbook pages 266-270)—what do they measure? What properties of hyperplanes are maximized by the objective function f(w) (on page 268) in the approach?

Similar presentations

Presentation is loading. Please wait....

OK

SVMs in a Nutshell.

SVMs in a Nutshell.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on any one mathematician rene Ppt on live line maintenance procedures Ppt on electric meter testing jobs Ppt on union budget 2013 Ppt on earth dam Ppt on human body Ppt on object oriented programming with c++ pdf Ppt on chapter 3 atoms and molecules elements Ppt on thermal conductivity of insulating powder coating Ppt on natural selection