The phrase "accuracy of learning" is correct and usable in written English.
It can be used when discussing the precision or correctness of the learning process or outcomes in educational contexts.
Example: "The accuracy of learning is crucial for ensuring that students grasp the material effectively."
Alternatives: "precision of learning" or "correctness of learning".
As a result, they achieved a significant advance in speed and accuracy of learning.
Boosting is a general method for improving the accuracy of learning algorithms.
The system has being evaluated in both the accuracy of learning concepts extraction and the degree of user satisfaction, as measured by questionnaires given to the teachers who tested the system.
As a method of data preprocessing of learning algorithm, feature extraction can better improve the accuracy of learning algorithm and shorten the time.
As a method of data preprocessing of the learning algorithm, feature extraction can better improve the accuracy of learning algorithm and shorten the time.
Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets.
We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm.
Despite the ensemble systems have been shown to be an efficient method to increase the accuracy and stability of learning algorithms in recent decades, its construction has a question to be elucidated: diversity.
Although independent scientific studies begun to demonstrate the accuracy of machine learning algorithms for translating social media data, such as Facebook likes, the content of blogs and Facebook updates, into relatively valid personality profiles, these data-scraping techniques may not be legitimately implemented in recruitment.
To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas.
Students presented research on a range of topics, from testing the accuracy of machine learning classifiers to identify stroke patients, to development of an augmented reality system for cerebral angiography, to the effect of race and socioeconomic status on outcomes after pediatric allogeneic hematopoietic cell transplantation.
