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BMCBI
2007
154views more  BMCBI 2007»
13 years 10 months ago
Classification of heterogeneous microarray data by maximum entropy kernel
Background: There is a large amount of microarray data accumulating in public databases, providing various data waiting to be analyzed jointly. Powerful kernel-based methods are c...
Wataru Fujibuchi, Tsuyoshi Kato
ICDM
2010
IEEE
115views Data Mining» more  ICDM 2010»
13 years 7 months ago
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times
Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for class...
Jin Shieh, Eamonn J. Keogh
SNPD
2010
13 years 11 months ago
Explaining Classification by Finding Response-Related Subgroups in Data
Abstract--A method for explaining results of a regressionbased classifier is proposed. The data is clustered using a metric extracted from the classifier. This way, clusters found ...
Elina Parviainen, Aki Vehtari
BMCBI
2005
167views more  BMCBI 2005»
13 years 9 months ago
Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes
Background: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer...
Patrick Warnat, Roland Eils, Benedikt Brors
3DPVT
2006
IEEE
197views Visualization» more  3DPVT 2006»
14 years 1 months ago
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, ...
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol...