This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e.,
Google Scholar Michalski, R. S. (1987). How to learn imprecise concepts: A method for employing a two-tiered knowledge representation in learning. InProceedings of the Fourth International Workshop on Machine Learning(pp. 50–58). Irvine, CA: Morgan Kaufmann. Google Scholar Quinlan, J. R. (...
We present HILLARY, an incremental learning method that addresses several of the more difficult aspects of learning from examples. Specifically, HILLARY employs 'hill climbing' to incrementally learn disjunctive concepts from noisy data in either a relational or at tribute-value representation. In the ...
The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among ...
based analyses allow one to (1) gain insights into the representation and composition of concepts in the model as well as quantitatively investigate their role in prediction, (2) identify and counteract Clever Hans filters8focusing on spurious correlations in the data, and (3) analyse whole ...
The definition and representation of exposed and non-exposed karst aquifers is illustrated in Fig. 2 Full size image Carbonate rocks (sedimentary or metamorphic) Evaporites Other sedimentary formations Other metamorphic rocks and igneous rocks Carbonate and evaporite rocks are further subdivided ...
As we encode the word "it", one attention head is focusing most on "the animal", while another is focusing on "tired" -- in a sense, the model's representation of the word "it" bakes in some of the representation of both "animal" and "tired". ...
representation in MaX-DeepLab, a latent code in the global context (instance code) and CNN feature maps to represent instance- and pixel-level features. Based on the representation, researchers introduce a cropping-free temporal fusion approach t...
In Proc. 35th International Conference on Machine Learning 50–59 (ICML, 2018). Chen, X. et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In Proc. 30th Conference on Advances in Neural Information Processing Systems 2172–2180 (NeurIPS, 2016...
global pooling, along the (height, width) of each feature map to obtain a representation of Φl(x) as a one-dimensional array of p elements. This solution, only briefly mentioned in [25], actually improves the quality of the regression fit by considering the spatial dependencies in the ...