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Titlebook: Advances in Knowledge Discovery and Data Mining; 27th Pacific-Asia Co Hisashi Kashima,Tsuyoshi Ide,Wen-Chih Peng Conference proceedings 202

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Overview of Hardware and Softwarean no longer be met by prevailing physical-based models and require new automatic solutions for planning, monitoring, and controlling tasks. In turn, artificial intelligence (AI) offers many necessary tools to develop such novel solutions. In this paper we take the first step towards bringing the ut
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Overview of Hardware and Software different degrees. Therefore, the more complex but informative label space makes it challenging to directly model the relationship between original features and label distributions. In this paper, an algorithm called Label Distribution Learning with Discriminative Instance Mapping (LDLDIM) is propo
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Antoine Geissbühler,Jean-Raoul Scherrerle performance on a range of applications on a large variety of datasets. On tabular datasets, . models are preferable when learning from large quantities of data as non-parametric models such as . and . cannot be easily used due to their inherent in-core data processing (i.e., they require loading
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HEGP Clinical Information System, samples, and ii) losing the explicit local properties due to global pooled features. Motivated by them, we propose a novel method to enhance robustness in few-shot learning by aligning prototypes with abundantly informed ones. As a way of providing more information, we smoothly augment the support
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