Sentence examples for model based transformation from inspiring English sources

To account for these issues, we proposed a two-stage mixture modeling strategy, the strength of which was built on the mixture model based transformation and the subsequent data integration on the poe scale.

A recent study proposed a Bayesian mixture model based transformation of DNA mi-croarray data with potential features applicable to meta-analysis of microarray studies [ 8].

We will also describe how these mapping rules can be implemented through meta-model based transformations, which can be used to map other languages to OWL.

Further by comparing to a global standardization method, the mixture model based data transformation demonstrated superior properties for data integration and provided solid basis for building classifiers at the second stage.

A Model Driven Architecture (MDA) based transformation methodology is presented first.

Targeting the existing assembly model expression methods' defects in terms of incompleteness of assembly information and perplexity of assembly relationship description, we proposed an assembly model based on constraint transformation, and introduced the model structure and modeling method for the model.

This paper proposes a stochastic model based on graph transformation with general probability distributions.

A new anisotropic ductile fracture model based on linear transformation of stresses is proposed to account for the out-of-plane fracture strength for AHSS sheets.

An ADE schema may be developed manually, or automatically derived from an UML model based on the transformation rules described in van den Brink et al. [36].

Descriptive analysis presents means and standard deviations, for the right skewed IgE-anti-TPO measurements medians and the interquartile range (IQR) was calculated.To achieve normal distributions for model based analyses, log transformation was applied if necessary.

In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series.