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Parzen in [42] formulated the estimation of PDFs using kernel approaches.
Interestingly, many graph kernel approaches from Computer Science share properties of traditional similarity measures for chemical compounds, like molecular fingerprints based on paths, cycles and subgraphs.
A few initial works have proposed ways to make persistent homology's outputs directly amenable to standard machine learning techniques (typically via kernel approaches [57 59]).
In this sense, some general frameworks for distributed kernel approaches have been presented in [6, 7] to solve non-linear regression in a distributed and real-time way.
We also have a short glimpse at kernel approaches and its dual, relational neural systems for general dissimilarity data, which extend neural networks from the simple vectorial setting to more complex data as inputs.
It has not escaped our notice that kernel approaches may hold a high potential for predicting the other protein features.
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Therefore, convolution kernel approach can be introduced to get K.
In the CVT method a kernel approach was used.
The kernel approach used does not exclude any additional volume of the lung in the analysis.
Based on convolution kernel approach, matrix K can be constructed by Equation (5).
Probabilistic localization methods, such as that presented in [7], use the kernel approach to approximate the conditional expectation E(c|s).
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