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» Robust Induction of Process Models from Time-Series Data
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BMCBI
2007
179views more  BMCBI 2007»
13 years 7 months ago
Automated smoother for the numerical decoupling of dynamics models
Background: Structure identification of dynamic models for complex biological systems is the cornerstone of their reverse engineering. Biochemical Systems Theory (BST) offers a pa...
Marco Vilela, Carlos Cristiano H. Borges, Susana V...
ESANN
2003
13 years 8 months ago
Self-organizing maps and functional networks for local dynamic modeling
The paper presents a method for times series prediction using a local dynamic modeling based on a three step process. In the first step the input data is embedded in a reconstruct...
Noelia Sánchez-Maroño, Oscar Fontenl...
CONTEXT
2007
Springer
14 years 1 months ago
Risk Context Effects in Inductive Reasoning: An Experimental and Computational Modeling Study
Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Experimental results indicate that people rate the same inductive reasoning argument dif...
Kayo Sakamoto, Masanori Nakagawa
PAMI
2008
182views more  PAMI 2008»
13 years 7 months ago
Gaussian Process Dynamical Models for Human Motion
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
Jack M. Wang, David J. Fleet, Aaron Hertzmann
CIDM
2007
IEEE
14 years 1 months ago
Analysis of streaming GPS measurements of surface displacement through a web services environment
Abstract— We present a method for performing mode classification of real-time streams of GPS surface position data. Our approach has two parts: an algorithm for robust, unconstr...
Robert Granat, Galip Aydin, Marlon E. Pierce, Zhig...