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Such a heterogeneous dataset requires a mixture of individual data descriptions.
In this paper, we present a mixture of support vector data descriptions (mSVDD) for the novelty detection task.
In this paper, we present a mixture of support vector data descriptions (mSVDD) for one-class classification or novelty detection.
Leveraging on the expectation maximization principle, we propose a mixture of support vector data descriptions for one-class classification or novelty detection problem.
Using automated code generation techniques that directly translate raw data descriptions of a given district into executable optimization code, the tool simplifies and accelerates the process of developing and executing district energy system optimizations, and visualizing/interpreting results.
Source Adapted from Gusmano et al. (2006) Variable Rationale Data descriptions 1. Older population size The degree of frailty increases with age, thereby leading to physical and cognitive decline and limited competence [that is, physical and mental health, intellectual capacity or ego strength (Lawton 1977)].
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Data description and harmonisation.
Section 2 provides data description.
First, we report our data description.
Section 4 provides detailed data description.
40173_2013_37_MOESM1_ESM.docx Additional file 1 Appendix A. Data description and sources.
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