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» Input Modeling Using Quantile Statistical Methods
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CVPR
2005
IEEE
14 years 9 months ago
Part-Based Statistical Models for Object Classification and Detection
We propose using simple mixture models to define a set of mid-level binary local features based on binary oriented edge input. The features capture natural local structures in the...
Elliot Joel Bernstein, Yali Amit
MICCAI
2008
Springer
14 years 8 months ago
Customized Design of Hearing Aids Using Statistical Shape Learning
3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient's anatomy. In this paper, we presen...
Gozde B. Unal, Delphine Nain, Gregory G. Slabaug...
WSC
2000
13 years 9 months ago
Simulation from non-standard distributions using envelope methods
This paper considers the development of envelope methods as a tool for simulation. Envelope methods are based on the construction of simple envelopes to functions. The proposed en...
Michael J. Evans, Tim B. Swartz
IDEAL
2000
Springer
13 years 11 months ago
Quantization of Continuous Input Variables for Binary Classification
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
Michal Skubacz, Jaakko Hollmén
BMCBI
2006
119views more  BMCBI 2006»
13 years 7 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt