Image de couverture de livre

Fractionally Supervised Classification with Maxima Nominated Samples

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Jozani, Mohammad Jafari, Wang, Jingyu
Format:	Preprint
Publié:	2026
Sujets:	Methodology Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Threshold Exceedance Estimation in Spatially Correlated Areal Data Using Maxima-Nominated Sampling
par: Jozani, Mohammad Jafari
Publié: (2026)

Elite-Driven Support Vector Machines for Classification
par: Jozani, Mohammad Jafari, et autres
Publié: (2026)

L-Estimation of Population Quantiles Using Ranked Set Sampling
par: Jozani, Mohammad Jafari, et autres
Publié: (2026)

Label Noise Cleaning for Supervised Classification via Bernoulli Random Sampling
par: Liu, Yuxin, et autres
Publié: (2026)

SHAP-Based Supervised Clustering for Sample Classification and the Generalized Waterfall Plot
par: Lin, Justin, et autres
Publié: (2025)

Leveraging Rank Information for Robust Regression Analysis: A Nomination Sampling Approach
par: Neve Loewen, et autres
Publié: (2026)

A Semi-Supervised Kernel Two-Sample Test
par: Lee, Gyumin, et autres
Publié: (2026)

Finite-Sample and Distribution-Free Fair Classification: Optimal Trade-off Between Excess Risk and Fairness, and the Cost of Group-Blindness
par: Hou, Xiaotian, et autres
Publié: (2024)

On Fractional Moment Estimation from Polynomial Chaos Expansion
par: Novák, Lukáš, et autres
Publié: (2024)

Supervised Manifold Learning for Functional Data
par: Tan, Ruoxu, et autres
Publié: (2025)

Self-Supervised Learning with Gaussian Processes
par: Duan, Yunshan, et autres
Publié: (2025)

Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
par: Mao, Xiaojun, et autres
Publié: (2024)

SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning
par: Shan, Jiawei, et autres
Publié: (2025)

Noise-Adaptive Conformal Classification with Marginal Coverage
par: Bortolotti, Teresa, et autres
Publié: (2025)

Task-tailored Pre-processing: Fair Downstream Supervised Learning
par: Sohn, Jinwon, et autres
Publié: (2026)

Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
par: Frusque, Gaëtan, et autres
Publié: (2023)

An Equal-Probability Partition of the Sample Space: A Non-parametric Inference from Finite Samples
par: Eriksson, Urban
Publié: (2025)

Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning
par: Matsubara, Takuo
Publié: (2024)

Classifier Pooling for Modern Ordinal Classification
par: Rotenberg, Noam H., et autres
Publié: (2026)

Active Sequential Two-Sample Testing
par: Li, Weizhi, et autres
Publié: (2023)

Sample-Efficient Omniprediction for Proper Losses
par: Gibbs, Isaac, et autres
Publié: (2025)

Learning from a Biased Sample
par: Sahoo, Roshni, et autres
Publié: (2022)

Conformal Risk Control for Ordinal Classification
par: Xu, Yunpeng, et autres
Publié: (2024)

Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification
par: Chen, Jiachen, et autres
Publié: (2024)

Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional
par: Li, Qiang, et autres
Publié: (2025)

E-Valuating Classifier Two-Sample Tests
par: Pandeva, Teodora, et autres
Publié: (2022)

Distribution-free Conformal Prediction for Ordinal Classification
par: Chakraborty, Subhrasish, et autres
Publié: (2024)

Classification Using Global and Local Mahalanobis Distances
par: Ghosh, Annesha, et autres
Publié: (2024)

Distribution Matching for Self-Supervised Transfer Learning
par: Jiao, Yuling, et autres
Publié: (2025)

MMDCP: A Distribution-free Approach to Outlier Detection and Classification with Coverage Guarantees and SCW-FDR Control
par: Lin, Youwu, et autres
Publié: (2025)

BFTS: Thompson Sampling with Bayesian Additive Regression Trees
par: Deng, Ruizhe, et autres
Publié: (2026)

MLCBART: Multilabel Classification with Bayesian Additive Regression Trees
par: Tian, Jiahao, et autres
Publié: (2026)

A Black-Box Debiasing Framework for Conditional Sampling
par: Cui, Han, et autres
Publié: (2025)

Advanced Tutorial: Label-Efficient Two-Sample Tests
par: Li, Weizhi, et autres
Publié: (2025)

Sample size determination for machine learning in medical research
par: Arifin, Wan Nor, et autres
Publié: (2025)

Causal Discovery and Classification Using Lempel-Ziv Complexity
par: Dhruthi, et autres
Publié: (2024)

Positive and Unlabeled Data: Model, Estimation, Inference, and Classification
par: Liu, Siyan, et autres
Publié: (2024)

Metropolis Sampling
par: Martino, Luca, et autres
Publié: (2017)

Efficient Online Variational Estimation via Monte Carlo Sampling
par: Chagneux, Mathis, et autres
Publié: (2026)

Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
par: Chen, Li, et autres
Publié: (2026)