March 24 2006
Project 1 due today. | |
Clustering example papers are: | |
Self organizing Map Java Demo |
February 24 2006
Project 1 Description is partially up on the project page, details will follow. | |
SVM Demo code is posted on the PR links page under SVM Topic |
February 13 2006
Homework 2 will be due on Wed. Feb 15. To change the parameter C in LIBSVM, use the parameter -c in the string: |
model = svmtrain(train_y,train_data,'-t 0 -b 1 -c 5');% sets C=5
List of all libsvm options:
-s svm_type : set type of SVM (default 0) 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- nu-SVR -t kernel_type : set type of kernel function (default 2) 0 -- linear: u'*v 1 -- polynomial: (gamma*u'*v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u'*v + coef0) -d degree : set degree in kernel function (default 3) -g gamma : set gamma in kernel function (default 1/k) -r coef0 : set coef0 in kernel function (default 0) -c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1) -n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) -p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1) -m cachesize : set cache memory size in MB (default 40) -e epsilon : set tolerance of termination criterion (default 0.001) -h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1) -b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1) The k in the -g option means the number of attributes in the input data. option -v randomly splits the data into n parts and calculates cross validation accuracy/mean squared error on them.
February 5 2006
Homework 2 is posted on the homework page. |
January 23 2006
Homework 1 Assignment: From textbook, 2.4,2.8. 2.28, 2.31 The last two problems are computer problems. You can use any language that you want. I suggest either R or Matlab. It is due on Jan 30 in class. |
January 20 2006
The papers and text sections for the week will be given here and on the syllabus page. | |
Week 2 Topics: Decision functions and Bayesian decision theory, metrics | |
Naive Bayes Classifiers, "On the Optimality of the Simple Bayesian Classifier under zero-one Loss", Domingos and Pazzani, Machine Learning, 1997; "An Empirical Study of the naive Bayes classifier," Rish in IJCAI-01 workshop on "Empirical Methods in AI". Also appeared as IBM Technical Report RC22230. | |
Application: Spam Detection |
January 9 2006
First meeting will be at 8 AM (way too early) in 1624 Howe, I would like to switch the class to two days/week, 1 hour 20 minute sessions if possible, but please bring schedule information. | |
I will be traveling on Friday Jan 13, Ms. Joset Etzel will cover class on Friday at 8 AM in Howe 1246. |
Last Updated: 04/10/2006