Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.
Radial basis function ker- nel optimization for Support Vector Machine classi- fiers,
2 Pith papers cite this work. Polarity classification is still indexing.
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Genetic algorithm optimization of QSVM kernel circuits produces encodings that match or exceed standard classical and quantum kernels on binary and multi-class datasets, with a reported positive correlation to kernel entropy.
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Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Presents a diversity-aware batch-mode query-by-committee active learning method using cosine similarity to select non-redundant queries for efficient stress-space sampling in data-driven constitutive modeling.
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Kernel Alignment for Quantum Support Vector Machines Using Genetic Algorithms
Genetic algorithm optimization of QSVM kernel circuits produces encodings that match or exceed standard classical and quantum kernels on binary and multi-class datasets, with a reported positive correlation to kernel entropy.