Search alternatives:
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
linear optimization » lead optimization (Expand Search), after optimization (Expand Search)
binary scale » binary image (Expand Search)
scale linear » scale linearly (Expand Search), simple linear (Expand Search), sparse linear (Expand Search)
ips derived » ipsc derived (Expand Search), its derived (Expand Search), hipsc derived (Expand Search)
binary ips » binary pairs (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
linear optimization » lead optimization (Expand Search), after optimization (Expand Search)
binary scale » binary image (Expand Search)
scale linear » scale linearly (Expand Search), simple linear (Expand Search), sparse linear (Expand Search)
ips derived » ipsc derived (Expand Search), its derived (Expand Search), hipsc derived (Expand Search)
binary ips » binary pairs (Expand Search)
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1
MCLP_quantum_annealer_V0.5
Published 2025“…This paper first proposes the QUBO-MCLP algorithm workflow and designs the Transformation Operator for Inequality Constraints Considering the Capacity of Accessible Providers (TOICCAP), which accounts for the scale of accessible supply points. …”
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2
Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
Published 2019“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”
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3
Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”