Results 91 to 100 of about 113,531 (269)
Scientific ReportsGiven that the decision tree C4.5 algorithm has outstanding performance in prediction accuracy on medical datasets and is highly interpretable, this paper carries out an optimization study on the selection of hyperparameters of the algorithm in order to ...
Yiyan Zhang, Yi Xin, Qin Li
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Collaborative hyperparameter tuning
, 2013 International audienceHyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches.
Bardenet, R. +3 more
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Advanced Science, EarlyView.This study generates high‐fidelity synthetic longitudinal records for a million‐patient diabetes cohort, successfully replicating clinical predictive performance. However, deeper analysis reveals algorithmic biases and trajectory inconsistencies that escape standard quality metrics. These findings challenge current validation norms, demonstrating why a
Francisco Ortuño +5 more
wiley +1 more source
Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham
Jambura Journal of MathematicsThe accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it ...
Fandi Presly Simamora +2 more
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ACHO: Adaptive Conformal Hyperparameter Optimization
, 2022 Several novel frameworks for hyperparameter search have emerged in the last decade, but most rely on strict, often normal, distributional assumptions, limiting search model flexibility. This paper proposes a novel optimization framework based on upper confidence bound sampling of conformal confidence intervals, whose weaker assumption of ...
openaire +2 more sources
Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model
Advanced Science, EarlyView.A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen +15 more
wiley +1 more source
Evaluation of Hyperparameter Optimization Techniques for Traditional Machine Learning Models [PDF]
Jisuanji kexueReasonable hyperparameters ensure that machine learning models can adapt to different backgrounds and tasks.In order to avoid the inefficiency caused by manual adjustment of a large number of model hyperparameters and a vast search space,various ...
LI Haixia, SONG Danlei, KONG Jianing, SONG Yafei, CHANG Haiyan
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Overtuning in Hyperparameter Optimization
Accepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Schneider, Lennart +2 more
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Hyperparameters: Optimize, or Integrate Out? [PDF]
, 1996 I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants. In the ‘evidence framework’ the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters.
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Advanced Science, EarlyView.Geometry and connectivity are complementary structures, which have demonstrated their ability to represent the brain's functional activity. This study evaluates geometric and connectome eigenmodes as biologically informed constraints for EEG source localization.
Pok Him Siu +6 more
wiley +1 more source