Anonymous Microsoft Web Data Data Set
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W. Nick Street and Yoo-Hyon Kim. A streaming ensemble algorithm (SEA) for large-scale classification. KDD. 2001.
cases, 25.7% of which belong to class 1. Predictive features include nuclear grade, tumor extent, tumor size, lymph node status, and number of lymph nodes examined. # anonymous Web browsing: This data set records browsing patterns for 32,710 anonymous visitors to the Microsoft Web site. We created a classification problem in a manner similar to Breese, et al.  by choosing to predict whether a
Dmitry Pavlov and Jianchang Mao and Byron Dom. Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR. 2000.
by the standard SVM training algorithms. 3. Experiments We compared performance of linear classifiers trained with the Boost-SMO and the Full-SMO (conventional SMO algorithm) on the following three data sets: the Reuters Data, the Microsoft Web Data and the UCI Adult Data. For the Reuters Data we looked at the classes "acq" and "earn" that have the greatest number of positive examples. The Microsoft
Dmitry Pavlov and Darya Chudova and Padhraic Smyth. Towards scalable support vector machines using squashing. KDD. 2000.
2: Error on the Test Set for Various Models On the Synthetic Data. Baseline 1% srs-SMO 1% squash-SMO 1% boost-SMO full-SMO 50% 41% 34.04% 34.84% 33.54% 8 4.2 Results on Benchmark Data The public datasets were ``The Microsoft Anonymous Web ' and the ''Forest Cover Type'' datasets available at UCI KDD archive [Bay99] and Adult dataset available at UCI machine learning repository [BM98]. Web data
Kristin P. Bennett and Erin J. Bredensteiner. Geometry in Learning. Department of Mathematical Sciences Rensselaer Polytechnic Institute.
that are publicly available via the World Wide Web All of the above datasets are available via anonymous file transfer protocol (ftp) from the UCI Repository of Machine Learning Databases and Domain Theories  at ftp://ftp.ics.uci.edu/pub/machine-learning-databases. The