188 search results - page 11 / 38 » Random Forests and Kernel Methods |
ICONIP
2008
13 years 9 months ago
2008
Abstract. The concept of Ensemble Learning has been shown to increase predictive power over single base learners. Given the bias-variancecovariance decomposition, diversity is char...
AAAI
2010
13 years 7 months ago
2010
Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to qua...
ICPR
2010
IEEE
14 years 1 months ago
2010
IEEE
—Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
MCS
2009
Springer
14 years 7 days ago
2009
Springer
Abstract. Data with multi-valued categorical attributes can cause major problems for decision trees. The high branching factor can lead to data fragmentation, where decisions have ...