Haberman's Survival Data Set
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Dennis DeCoste. Anytime Query-Tuned 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 , Sonar and Haberman and the MNIST digit-recognition dataset . We confirmed that L k (x) # f(x) # H k (x) always held. Table 3 summarizes some of our results. Rows labelled 1-2 summarize
Dennis DeCoste. Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry. ICML. 2002.
for FL = FH using all training data as queries. Rows 23-24 report the same for when w + ,w (recall Equation 34) are not used as S 1 ,S 2 . Rows 31-34 similarly report both cases for a second (test) dataset. For the small Sonar and Haberman this second set is the non-SV 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 out-of-bag 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 : Autompg, Bupa, Glass, Haberman Housing, Cleveland-heart-disease, Hepatitis, Ion, Pima, Sonar, Vehicle, WDBC, Wine and WPBC. Some of the data sets do not originally depict two-class 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 hayes-roth 0.32 0.32 0 0.01 6 6 132 monks-3 0.83 0.24 0.82 0 7 7 552 monks-1 0.98 0 0.98 0 7 7 554 monks-2 0.48 0.48 0.21 0 7 7 600