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Published in 2018 at "Machine Learning"
DOI: 10.1007/s10994-018-5724-2
Abstract: For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. “Dirty” non-curated data give rise to categorical variables with a very…
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Keywords:
encoding learning;
similarity encoding;
similarity;
categorical variables ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3145195
Abstract: In classification tasks, training labels are usually specified as one-hot targets which represent each class equally and exclusively. However, this labeling rule is not suitable in some situations. For the dependent classes, one-hot targets are…
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Keywords:
one hot;
classification;
channel encoding;
soft label ... See more keywords
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Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3571404
Abstract: This study proposes theories and applications of probabilistic divergences to neural network training. This theory generalizes the cross-entropy method for backpropagation to the alpha-divergence method. This new method includes the cross-entropy method as a limited…
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Keywords:
method;
probability;
quasi one;
one hot ... See more keywords
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Published in 2025 at "Czech Polar Reports"
DOI: 10.5817/cpr2025-1-9
Abstract: This study summarizes the lichen diversity in the Colesdalen area of Svalbard, where a total of 234 species are known. Notably, 112 of these lichen species are reported for the first time in this region.…
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Keywords:
area;
one hot;
vicinity colesdalen;
lichens vicinity ... See more keywords