Sciweavers

CLEF
2010
Springer
13 years 5 months ago
ImageCLEF 2010 Working Notes on the Modality Classification Subtask
The goal of this work is to investigate the performance of classical methods for feature description and classification, and to identify the difficulties of the ImageCLEF 2010 moda...
Olivier Pauly, Diana Mateus, Nassir Navab
ICMLA
2007
13 years 6 months ago
Estimating class probabilities in random forests
For both single probability estimation trees (PETs) and ensembles of such trees, commonly employed class probability estimates correct the observed relative class frequencies in e...
Henrik Boström
ICMLA
2008
13 years 6 months ago
Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous
Using decision trees that split on randomly selected attributes is one way to increase the diversity within an ensemble of decision trees. Another approach increases diversity by ...
Michael Gashler, Christophe G. Giraud-Carrier, Ton...
ICMLA
2008
13 years 6 months ago
Calibrating Random Forests
When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of...
Henrik Boström
ICPR
2010
IEEE
13 years 8 months ago
Fast and Spatially-Smooth Terrain Classification Using Monocular Camera
In this paper, we present a monocular camera based terrain classification scheme. The uniqueness of the proposed scheme is that it inherently incorporates spatial smoothness while...
Chetan Jakkoju, Madhava Krishna, C. V. Jawahar
ILP
2004
Springer
13 years 10 months ago
First Order Random Forests with Complex Aggregates
Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well...
Celine Vens, Anneleen Van Assche, Hendrik Blockeel...
IDA
2007
Springer
13 years 10 months ago
Combining Bagging and Random Subspaces to Create Better Ensembles
Random forests are one of the best performing methods for constructing ensembles. They derive their strength from two aspects: using random subsamples of the training data (as in b...
Pance Panov, Saso Dzeroski
IDEAS
2007
IEEE
128views Database» more  IDEAS 2007»
13 years 10 months ago
Streaming Random Forests
Many recent applications deal with data streams, conceptually endless sequences of data records, often arriving at high flow rates. Standard data-mining techniques typically assu...
Hanady Abdulsalam, David B. Skillicorn, Patrick Ma...
JCDL
2009
ACM
179views Education» more  JCDL 2009»
13 years 11 months ago
Disambiguating authors in academic publications using random forests
Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and...
Pucktada Treeratpituk, C. Lee Giles
ICDM
2009
IEEE
113views Data Mining» more  ICDM 2009»
13 years 11 months ago
Spatiotemporal Relational Random Forests
Abstract—We introduce and validate Spatiotemporal Relational Random Forests, which are random forests created with spatiotemporal relational probability trees. We build on the do...
Timothy A. Supinie, Amy McGovern, John Williams, J...