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» Support Vector Classification with Input Data Uncertainty
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UIST
2010
ACM
13 years 5 months ago
A framework for robust and flexible handling of inputs with uncertainty
New input technologies (such as touch), recognition based input (such as pen gestures) and next-generation interactions (such as inexact interaction) all hold the promise of more ...
Julia Schwarz, Scott E. Hudson, Jennifer Mankoff, ...
DMIN
2008
145views Data Mining» more  DMIN 2008»
13 years 9 months ago
Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels
We propose a novel privacy-preserving nonlinear support vector machine (SVM) classifier for a data matrix A whose columns represent input space features and whose individual rows ...
Olvi L. Mangasarian, Edward W. Wild
TKDD
2008
113views more  TKDD 2008»
13 years 7 months ago
Privacy-preserving classification of vertically partitioned data via random kernels
We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns are divided into groups belonging to different entiti...
Olvi L. Mangasarian, Edward W. Wild, Glenn Fung
IJON
2006
119views more  IJON 2006»
13 years 7 months ago
Support vector machine for functional data classification
Abstract. Functional data analysis is a growing research field and numerous works present a generalization of the classical statistical methods to function classification or regres...
Fabrice Rossi, Nathalie Villa
3DPVT
2006
IEEE
197views Visualization» more  3DPVT 2006»
13 years 11 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...