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A Definable Interpretation of Metric Spaces
A Definable Interpretation of Metric Spacesopenaire +1 more source
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The spacing effect: Improving electrocardiogram interpretation
The Clinical Teacher, 2023Abstract Background Accurate electrocardiogram (ECG) interpretation is critical for safe patient care, making this skill a necessary competency for medical school graduation. Improved long‐term memory retention with repeated exposure to material is one of the most evidenced‐based components of ...
John M. Cunningham +6 more
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A state-space interpretation of simultaneous stabilization
IEEE Transactions on Automatic Control, 1988A state-space interpretation is given of some previously known results on simultaneous stabilization.
M Vidyasagar
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Self, Space, and Interpretive Experience: The Interactionism of Environmental Interpretation
Journal of Interpretation Research, 2003 In recent years there has been concern expressed for the need to strengthen and broaden the theoretical foundations of interpretation research and practice. Drawing on interactionist theory and sociological and spatial perspectives, the paper seeks to address this concern by proposing an alternative approach to environmental interpretation that moves ...
Stephen Wearing
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A Compositional and Interpretable Semantic Space
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion
Alona Fyshe +4 more
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An interpretation of Mahalanobis distance in the dual space
Pattern Recognition, 1982Abstract Using the concept of a dual space, nk-dimensional vectors can be viewed as k points in an n-dimensional co-ordinate system. The relationships between the basic statistical properties of a k-variate sample and the geometrical properties of such a space are developed and the concept extended to two samples drawn from different populations ...
S J Roberts, R Hanka
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Feature space interpretation of SVMs with indefinite kernels [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005 Kernel methods are becoming increasingly popular for various kinds of machine learning tasks, the most famous being the support vector machine (SVM) for classification. The SVM is well understood when using conditionally positive definite (cpd) kernel functions. However, in practice, non-cpd kernels arise and demand application in SVMs.
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