Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
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The CPA-PA metric approximates ground-truth neural activity via CCA alignment and participant averaging, yielding 300-1000% better single-participant evaluations than conventional scores on synthetic and real MEEG data.
Improper use of test data during hyperparameter tuning in link prediction inflates performance estimates by an average of 3.6 percent across 60 networks, as measured by a new Loss Ratio metric.
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.
citing papers explorer
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Benchmarking Quantum Kernel Support Vector Machines Against Classical Baselines on Tabular Data: A Rigorous Empirical Study with Hardware Validation
Quantum kernel methods show no statistically significant edge over strong classical baselines on tabular classification tasks, with current feature maps failing to match the spectral properties of the best classical kernel.
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Robust Evaluation of Neural Encoding Models via ground-truth approximation
The CPA-PA metric approximates ground-truth neural activity via CCA alignment and participant averaging, yielding 300-1000% better single-participant evaluations than conventional scores on synthetic and real MEEG data.
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Impacts of Data Splitting Strategies on Parameterized Link Prediction Algorithms
Improper use of test data during hyperparameter tuning in link prediction inflates performance estimates by an average of 3.6 percent across 60 networks, as measured by a new Loss Ratio metric.
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Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
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Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution
Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.