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Ng's initial machine learning class became Coursera's first class back in 2011.
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The two met at Carnegie Mellon through machine learning classes before they went on to apply that knowledge at Instagram and Uber.
In his time since departing as Baidu's chief scientist, Ng has been developing a sequel of sorts to his popular introductory machine learning Coursera class.
We present a study using one-class machine learning for microRNA (miRNA) discovery and compare one-class to two-class approaches using naïve Bayes and Support Vector Machines.
Using the EBV genome as and external validation of the method we found one-class machine learning to work as well as or better than a two-class approach in identifying true miRNAs as well as predicting new miRNAs.
In addition, we demonstrate their superiority over commonly used two-class machine learning approaches such as Decision Trees and Bayesian Networks that has been used in the same context within past solutions.
We and others have described the use of two-class machine learning to identify novel miRNAs.
In a recent publication we described a two-class machine learning approach for miRNA prediction using the naïve Bayes classifier [ 13].
Machine learning techniques for multi-class classification [ 269, 270] (e.g. separating core vs. rim vs. surface) also hold potential for improvement.
In order to explore the potential of different classes of machine learning algorithms to learn distinctive features of ncRNA classes, we employed graph properties as input parameters to a variety of machine learning methods.
Fuzzy pattern trees (FPTs) have recently been introduced as a novel model class for machine learning.
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