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In this paper, we compare the MIMLwel algorithm with four state-of-the-art MIML algorithms, that is, MIMLkNN [ 31], MIMLNN [ 12], MIMLRBF [ 32], and MIMLSVM [ 5], under different configuration of weak-label ratios (W.L.R). on the Geobacter sulfurreducens dataset (Table 4) and Shewanella loihica PV-4 dataset (Table 5).
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Six ADTs are conducted under different configurations of the stressors.
We also discuss the hydrodynamic interaction between the two turbines under different configurations of the system.
The new approach was first evaluated on simulated data for its performance under different configurations of effect size and dimensionality.
Borrowing a central measurement from information theory, Shannon entropy, we quantified the uncertainties produced by decisions of participants within an economic decision task under different configurations of reward probability and time.
The objective of this study was to explain changes in the exposure rate (ER) of red deer to Anaplasma spp. under different configurations of suitable habitat and landscape fragmentation in the presence of variable densities of the potentially diluting host, wild boar.
The objective of this study was to develop a novel, lattice-derived, behavior-based, spatially-explicit model to explain changes in the ER of red deer to Anaplasma spp. under different configurations of suitable habitat and landscape fragmentation in the presence of variable densities of the potentially diluting host, wild boar.
A novel, lattice-derived, behavior-based, spatially-explicit model was developed to explain changes in deer Anaplasma spp. ER under different configurations of suitable habitat and landscape fragmentation in the presence of variable densities of the potentially diluting host, wild boar.
The aim of this study is to introduce the concept of Shannon entropy in decision making paradigms as a decision uncertainty descriptor of the task and to map the functional fingerprint of such uncertainty using an economic decision task under different configurations of probability and time.
We first apply our fish-swarm logic regression (FSLR) approach on a real screening dataset and then apply it on a series of simulated datasets under different configurations to test the performance of our approach compared to other logic regression-based approaches.
The result could be a radically different configuration of apparatus, queues and sensibilities.
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