Reconciling modern machine-learning practice and the classical bias–variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
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- Understanding deep learning (still) requires rethinking generalization→Reconciling modern machine-learning practice and the classical bias–variance trade-offLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Greedy function approximation: A gradient boosting machine.Legacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Advances in Neural Information Processing Systems 19Legacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Advances in neural information processing systems 7Legacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Advances in Neural Information Processing Systems 29Legacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Gaussian Processes in Machine LearningLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Random Features for Large-Scale Kernel MachinesLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Understanding deep learning (still) requires rethinking generalizationLegacy edge — source detail unavailable
- Reconciling modern machine-learning practice and the classical bias–variance trade-off→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
- Greedy function approximation: A gradient boosting machine.→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
- Boosting the margin: a new explanation for the effectiveness of voting methods→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
- A training algorithm for optimal margin classifiers→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Understanding deep learning (still) requires rethinking generalization | 2021 | 2,370 |
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Topics
| Machine Learning and Data Classification | Computer Science |
| Gaussian Processes and Bayesian Inference | Computer Science |
| Neural Networks and Applications | Computer Science |
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