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» Resampling methods for input modeling
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ICML
2006
IEEE
14 years 8 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
FM
2006
Springer
113views Formal Methods» more  FM 2006»
13 years 11 months ago
Interface Input/Output Automata
We propose a new look at one of the most fundamental types of behavioral interfaces: discrete time specifications of communication--directly related to the work of de Alfaro and H...
Kim Guldstrand Larsen, Ulrik Nyman, Andrzej Wasows...
TVCG
2011
170views more  TVCG 2011»
13 years 2 months ago
Feature-Preserving Volume Data Reduction and Focus+Context Visualization
— The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context ...
Yu-Shuen Wang, Chaoli Wang, Tong-Yee Lee, Kwan-Liu...
BMCBI
2010
149views more  BMCBI 2010»
13 years 7 months ago
A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context
Background: Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex eti...
Ioannis K. Valavanis, Stavroula G. Mougiakakou, Ke...
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