"machine learning classifiers" is a correct and usable phrase in written English.
It refers to algorithms or models that are trained on data to make predictions or decisions based on that data. Example: "The accuracy of our spam detection system was greatly improved after we incorporated machine learning classifiers into the algorithm."
Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets.
The purpose of this method is to filter out false positives incorrectly classified as relevant chemical entities by the machine learning classifiers.
All machine learning classifiers directly or indirectly rely on words and words' frequencies to classify a document.
Pereira, F., Mitchell, T. & Botvinick, M. Machine learning classifiers and fMRI: a tutorial overview.
Many machine learning classifiers are trained on data labeled by humans.
The first ghost arises from difficulties in determining what information machine learning classifiers use for decoding.
Aaron argues that the problem lies in the machine learning classifiers that sit underneath systems like Huggle.
This paper presents how commonly used machine learning classifiers can be analyzed using a common framework of convex optimization.
We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy.
Developing new machine learning classifiers is *hard* and so people with an interest to develop new machine learning systems from different standpoints have struggled to successfully complete them.
Student research topics ranged from machine learning classifiers to identify stroke patients to the effect of race and socioeconomic status on outcomes after pediatric allogeneic hematopoietic cell transplantation.
