Introduces geometric-sensitivity and active-set-instability signals to adaptively allocate measurements for kernel SVMs under Bernoulli noise, with theory and synthetic/quantum-kernel experiments showing improved margin and support-vector recovery.
Libsvm: A library for support vector machines,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
SMTPP decouples variance reduction from graph connectivity in push-pull decentralized optimization and guarantees convergence with a compressed steady-state error floor on any strongly connected directed graph.
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.
citing papers explorer
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Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations
Introduces geometric-sensitivity and active-set-instability signals to adaptively allocate measurements for kernel SVMs under Bernoulli noise, with theory and synthetic/quantum-kernel experiments showing improved margin and support-vector recovery.
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Stochastic Momentum Tracking Push-Pull for Decentralized Optimization over Directed Graphs
SMTPP decouples variance reduction from graph connectivity in push-pull decentralized optimization and guarantees convergence with a compressed steady-state error floor on any strongly connected directed graph.
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Large-Scale Quantum Kernels for Hyperspectral Data Classification
Simulated fidelity quantum kernels achieve competitive or better accuracy than RBF kernels on Indian Pines binary and multiclass tasks and Methane Detection data without heavy dimensionality reduction.