Sentence examples for second machine learning from inspiring English sources

In 2012, the second machine learning competition in immunology was performed [ 35], and it provided an unbiased comparison of different methods on previously unpublished data in a blind prediction setting.

Second, machine learning methods, which tune their parameters to the data, (SVM, RF, IRV, and PS-IRV) on average outperform the methods that are not tuned to the data (MaxSim, MeanSim, and 11NN), and have similar performances amongst each other.

This is the third machine learning startup Twitter has acquired, after Whetlab last year, and Madbits in 2014.

Multiple-instance learning is first proposed by Dietterich et al. [12], and it is the fourth machine learning technique besides supervised learning, unsupervised learning, and reinforcement learning.

Methods: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM.

Results: We propose a first machine learning approach to integrate multiple types of DTIs and predict unknown drug-target relationships or drug modes of action.

First, machine learning is computational not just because it is computer-intensive, but because it relies algorithmic procedures rather than causal models.b Second, the key activity is data exploration in ways that can be surprisingly thorough.

In this article we introduce DNorm, the first machine learning method to normalize disease names in biomedical text.

The second is machine learning method based on some selected channel parameters.

First, because Google Video is not much good now doesn't mean it won't get better: after all, when Blinkx was founded, it first applied machine learning to searching the desktops of personal computers, a project that was abandoned when Google and Microsoft released their own desktop search bars.

With these internal tools, or potentially with third-party machine learning platforms that are able to integrate tightly into their existing infrastructures, organizations can realize the potential of AI.