Sciweavers

30 search results - page 1 / 6
» Speeding Up Logistic Model Tree Induction
Sort
View
PKDD
2005
Springer
142views Data Mining» more  PKDD 2005»
14 years 4 months ago
Speeding Up Logistic Model Tree Induction
Logistic Model Trees have been shown to be very accurate and compact classifiers [8]. Their greatest disadvantage is the computational complexity of inducing the logistic regressi...
Marc Sumner, Eibe Frank, Mark A. Hall
ECML
2003
Springer
14 years 4 months ago
Logistic Model Trees
Abstract. Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and continuous numeric values. F...
Niels Landwehr, Mark Hall, Eibe Frank
SDM
2009
SIAM
149views Data Mining» more  SDM 2009»
14 years 8 months ago
Speeding Up Secure Computations via Embedded Caching.
Most existing work on Privacy-Preserving Data Mining (PPDM) focus on enabling conventional data mining algorithms with the ability to run in a secure manner in a multi-party setti...
K. Zhai, W. K. Ng, A. R. Herianto, S. Han
FMCAD
2007
Springer
14 years 5 months ago
Exploiting Resolution Proofs to Speed Up LTL Vacuity Detection for BMC
—When model-checking reports that a property holds on a model, vacuity detection increases user confidence in this result by checking that the property is satisfied in the inte...
Jocelyn Simmonds, Jessica Davies, Arie Gurfinkel, ...
IFIP12
2008
14 years 18 days ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unse...
Frederic T. Stahl, Max A. Bramer, Mo Adda