"Negative training creates little monsters," Paramithis said.
One of the biggest concerns that skeptics voice is the danger of so-called negative training.
To summarize, we used SEs as positive and TEs as negative training sets in mESC.
Negative training led to increased negative interpretation bias relative to other groups.
Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples.
One hundred thirty-five undergraduate students were randomized to receive positive training, negative training, or a control condition.
So I had an entirely negative training, and that's probably the best kind there is: my formal literary education was 'I will never, ever write like that'.
As mentioned before, negative training data was also needed.
Especially, the negative training datasets are very different among studies.
For negative training data, we assign an equivalent initial weight.
If you aren't sure, you risk providing negative training by giving bad information".
