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There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills, the difficulty of continual learning, the risk of catastrophic forgetting, the leaking of data privacy/proprietary, etc.
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R. E. Schapire · 1990
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Input space versus feature space in kernel-based methods
B. Schölkopf, S. Mika, C. J. Burges, P. Knirsch, K. Müller, G. Rätsch, and A. J. Smola · 1999
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NeC4.5: Neural ensemble based C4.5
Z.-H. Zhou and Y. Jiang · 2004
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
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Ensemble Methods: Foundations and Algorithms
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Stability and hypothesis transfer learning
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Efficient optimization of performance measures by classifier adaptation
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Mixture proportion estimation via kernel embeddings of distributions
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Attention is all you need
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Boosting-based reliable model reuse
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Handling concept drift via model reuse
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INFaaS: Automated model-less inference serving
F. Romero, Q. Li, N. J. Yadwadkar, and C. Kozyrakis · 2021
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Towards enabling learnware to handle unseen jobs
Y.-J. Zhang, Y.-H. Yan, P. Zhao, and Z.-H. Zhou · 2021
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A continual learning survey: Defying forgetting in classification tasks
M. Delange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, and T. Tuytelaars · 2022
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Discrepancy, coresets, and sketches in machine learning
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Domain agnostic learning with disentangled representations
X. Peng, Z. Huang, X. Sun, and K. Saenko · 2019
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Language models are few-shot learners
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A survey on multi-task learning
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Open-environment machine learning
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Model reuse with reduced kernel mean embedding specification
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Identifying helpful learnwares without examining the whole market
Y. Xie, Z.-H. Tan, Y. Jiang, and Z.-H. Zhou · 2023
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