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» Classifier Selection Based on Data Complexity Measures
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BMCBI
2008
169views more  BMCBI 2008»
14 years 10 months ago
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
Background: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular sig...
Alexander R. Statnikov, Lily Wang, Constantin F. A...
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
15 years 10 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
EDBT
2009
ACM
113views Database» more  EDBT 2009»
15 years 4 months ago
Type-based categorization of relational attributes
In this work we concentrate on categorization of relational attributes based on their data type. Assuming that attribute type/characteristics are unknown or unidentifiable, we an...
Babak Ahmadi, Marios Hadjieleftheriou, Thomas Seid...
WACV
2005
IEEE
15 years 3 months ago
Ensemble Methods in the Clustering of String Patterns
We address the problem of clustering of contour images from hardware tools based on string descriptions, in a comparative study of cluster combination techniques. Several clusteri...
André Lourenço, Ana L. N. Fred
ICML
2003
IEEE
15 years 11 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty