One major outcome of this project has been a model transformation technology (see figure below) that has applications to model optimization, scalable model construction, joint management of product families, design refactoring, and workflow automation.
In particular, we discuss model transformation, model ontologies, and multimodeling, with applications to construction of large models (e.g. MapReduce), model optimization, and consistency management across multiple models.
Lecture 5 (February 6): Machine learning abstractions: application/data, model, optimization problem, optimization algorithm.
Lecture 5 (February 3): Machine learning abstractions: application/data, model, optimization problem, optimization algorithm.
Cartwright, L. et al. In vitro placental model optimization for nanoparticle transport studies.
It involves facial feature representation, collecting training data, building regression/classification models and model optimization.
The optimization is further described in Deformable Model Optimization section.
Model optimization may lead to a more predictive model.
Image registration is typically formulated as an optimization process, which aims to find the optimal transformation parameters of a given transformation model by minimizing a cost function.
The aim is to create a single enriched model reduction transformation that preserves the precision of the reduced substructure model throughout the optimization process.
