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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...
ESWS
2010
Springer
13 years 10 months ago
Efficient Semantic-Aware Detection of Near Duplicate Resources
Abstract. Efficiently detecting near duplicate resources is an important task when integrating information from various sources and applications. Once detected, near duplicate reso...
Ekaterini Ioannou, Odysseas Papapetrou, Dimitrios ...
FLAIRS
2007
13 years 9 months ago
Managing Dynamic Contexts Using Failure-Driven Stochastic Models
We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three lay...
Nikita A. Sakhanenko, George F. Luger, Carl R. Ste...
ECIR
2003
Springer
13 years 8 months ago
From Uncertain Inference to Probability of Relevance for Advanced IR Applications
Uncertain inference is a probabilistic generalisation of the logical view on databases, ranking documents according to their probabilities that they logically imply the query. For ...
Henrik Nottelmann, Norbert Fuhr
NIPS
2004
13 years 8 months ago
Spike Sorting: Bayesian Clustering of Non-Stationary Data
Spike sorting involves clustering spike trains recorded by a microelectrode according to the source neuron. It is a complicated problem, which requires a lot of human labor, partl...
Aharon Bar-Hillel, Adam Spiro, Eran Stark