CG
2005
Springer
13 years 4 months ago
2005
Springer
In Monte-Carlo photon-tracing methods energy-carrying particles are traced in an environment to generate hit points on object surfaces for simulating global illumination. The surf...
TIP
2008
13 years 4 months ago
2008
Abstract--In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constrain...
TIP
2008
13 years 4 months ago
2008
Density estimation for observational data plays an integral role in a broad spectrum of applications, e.g. statistical data analysis and information-theoretic image registration. ...
CSDA
2007
13 years 4 months ago
2007
In this paper we study the problem of bivariate density estimation. The aim is to find a density function with the smallest number of local extreme values which is adequate with ...
CSDA
2006
13 years 4 months ago
2006
: Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its un...
ICANN
2010
Springer
13 years 5 months ago
2010
Springer
Abstract. We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data "resolution". Parameters of th...
UAI
2000
13 years 5 months ago
2000
Decision theory does not traditionally include uncertainty over utility functions. We argue that the a person's utility value for a given outcome can be treated as we treat o...
NIPS
2001
13 years 6 months ago
2001
We suggest a nonparametric framework for unsupervised learning of projection models in terms of density estimation on quantized sample spaces. The objective is not to optimally re...
NIPS
2007
13 years 6 months ago
2007
A method is proposed for semiparametric estimation where parametric and nonparametric criteria are exploited in density estimation and unsupervised learning. This is accomplished ...
NIPS
2007
13 years 6 months ago
2007
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likeli...