Sentence examples for machine learning data set from inspiring English sources

One excellent example is Yahoo's offer of the largest machine learning data set in history, taken from its own internal user activity logs.

A series of numeric studies are reported using data coming from UCI machine learning data sets.

This study is illustrated with the aid of machine learning data sets.

In order to validate the proposed tree structure it has been deployed on synthetic and machine learning data sets.

The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.

Machine learning workers support the significance analysis of single components (i.e. features) of an input vector to obtain smaller inputs with a reduced set of components/features, the partitioning of machine learning data into training and test sets, the construction of input/output mapping model functions and model based predictions as well as result visualization.

The performance of OMLA has been compared with both the existing online learning and batch learning algorithms for spiking neural networks using the UCI machine learning benchmark data sets.

Experimental results on UCI machine learning repository data sets reveal that the computational time for summarizing large text collections is drastically reduced using the MapReduce framework and MapReduce provides scalability for accommodating large text collections for summarizing.

Compared to some other classical problems in machine learning, microarray data sets pose various problems.

"We were working together a lot to try to process and analyze the large amount of data that we were collecting, and we had this idea of applying machine learning technology to our data set to see if we could detect if conflict was occurring between couples and levels greater than chance".

Though it is not currently available, there has been ongoing research and development at Berkeley's AMP lab on a platform called MLbase, which wraps MLlib, Spark, and other projects to make machine learning on data sets of all sizes accessible to a broader range of users [134 136].