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EJASP
2010
133views more  EJASP 2010»
13 years 5 months ago
Improving Density Estimation by Incorporating Spatial Information
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density esti...
Laura M. Smith, Matthew S. Keegan, Todd Wittman, G...
PAKDD
2005
ACM
142views Data Mining» more  PAKDD 2005»
14 years 4 months ago
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each o...
Andy M. Yip, Chris H. Q. Ding, Tony F. Chan
PG
2000
IEEE
14 years 3 months ago
A New Adaptive Density Estimator for Particle-Tracing Radiosity
Inparticle-tracing radiosityalgorithms, energy-carrying particles are traced through an environmentfor simulating global illumination. Illumination on a surface is reconstructed f...
Wong Kam Wah
ICASSP
2011
IEEE
13 years 2 months ago
Automatic audio tag classification via semi-supervised canonical density estimation
We propose a novel semi-supervised method for building a statistical model that represents the relationship between sounds and text labels (“tags”). The proposed method, named...
Jun Takagi, Yasunori Ohishi, Akisato Kimura, Masas...
ICARCV
2002
IEEE
110views Robotics» more  ICARCV 2002»
14 years 3 months ago
A novel robust method for large numbers of gross errors
In computer vision tasks, it frequently happens that gross noise occupies the absolute majority of the data. Most robust estimators can tolerate no more than 50% gross errors. In ...
Hanzi Wang, David Suter