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In addition, a problem of binary integer linear programming is solved to obtain and optimally place the minimum number of μPMUs necessary to provide a unique solution for the proposed state estimation formulation.
The development of prediction models then becomes a problem of binary classification, even though often multiple differential diagnoses exist.
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This study is motivated by a problem of tomographic reconstruction of binary axially symmetric objects, for which we have available one single blurred and noised snapshot.
Sometimes interoperability and composition mechanisms provided by component based integration frameworks cannot solve the problem of binary component integration in an automatic way.
An arithmetic word problem of binary operation is integration of two independent quantity sentences and one relative quantity sentence.
To the best of our knowledge, this paper is the first publication to consider the complete-blind recognition problem of binary cyclic codes in soft-decision situations.
Because the SVM classifier is originally designed to solve the problem of binary classification, it cannot be directly applied in multiclass classification which is usually encountered in practice [ 23].
It decomposes an M-class problem into a series of binary problems.
For feature selection of multiclass data, Ramaswamy et al. used an one-versus-all strategy to convert the multiclass problem into a series of binary class problems and applied SVM-RFE to each binary class problem separately (Ramaswamy et al., 2001. Fu and Fu-Liu (2005) also proposed a method based on the one-versus-all strategy.
Our results demonstrate that using the SSVM is more effective than the traditional approach of decomposing the problem into a collection of binary classification problems.
In our study, we apply linear classifiers on this high-dimensional classification problem, and apply the one-versus-the-rest (Bishop et al., 2006) method to convert the multiclass classification problem into a series of binary class problems.
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