Haberman's Survival Data Set
Below are papers that cite this data set, with context shown.
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Dennis DeCoste. Anytime QueryTuned Kernel Machines via Cholesky Factorization. SDM. 2003.
signs against minWz n 's typically less aggressive but steady improvements. Other hybrids are likely even better and worthy of future research. 5 Examples We checked our approach on two UCI datasets [1], Sonar and Haberman and the MNIST digitrecognition dataset [10]. We confirmed that L k (x) # f(x) # H k (x) always held. Table 3 summarizes some of our results. Rows labelled 12 summarize
Dennis DeCoste. Anytime IntervalValued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry. ICML. 2002.
for FL = FH using all training data as queries. Rows 2324 report the same for when w + ,w (recall Equation 34) are not used as S 1 ,S 2 . Rows 3134 similarly report both cases for a second (test) dataset. For the small Sonar and Haberman this second set is the nonSV training examples, demonstrating that examples farther from the discriminant hyperplane often require much smaller k. Our Haberman
Yin Zhang and W. Nick Street. Bagging with Adaptive Costs. Management Sciences Department University of Iowa Iowa City.
and the outofbag margin estimation will result in better generalization as it does in stacking. 3. Computational Experiments Bacing was implemented using MATLAB and tested on 14 UCI repository data sets [2]: Autompg, Bupa, Glass, Haberman Housing, Clevelandheartdisease, Hepatitis, Ion, Pima, Sonar, Vehicle, WDBC, Wine and WPBC. Some of the data sets do not originally depict twoclass problems
Denver Dash and Gregory F. Cooper. Model Averaging with Discrete Bayesian Network Classifiers. Decision Systems Laboratory Intelligent Systems Program University of Pittsburgh.
to emphasize the fact that AMA was typiData set ± SNN ± GTT ± NMA ± AMA N k Nr haberman 0.35 0.35 0 0 4 4 306 hayesroth 0.32 0.32 0 0.01 6 6 132 monks3 0.83 0.24 0.82 0 7 7 552 monks1 0.98 0 0.98 0 7 7 554 monks2 0.48 0.48 0.21 0 7 7 600
