Sentence examples for machine learning implementation from inspiring English sources

And it's not alone in that: every major machine learning implementation is available for free to use and modify, meaning it's possible to set up a simple machine intelligence with nothing more than a laptop and a web connection.

Startups will be judged on the uniqueness of their underlying machine learning implementation.

Although no attempts of tuning the performance of the method for the sake of maximal generality, the descriptor vectors are very easily amenable to machine learning implementation, which is expected to greatly enhance the discrimination power of the method (albeit at the cost of generality and the requirement of a training set).

If you look at machine learning implementations in the real world, they almost always use a technique called active learning to more efficiently collect data.

We use the following features in a machine-learning implementation: number of calls, number of messages, and number of collocations.

All machine learning technique implementations stem from a fully automated data analysis framework Glaab et al. (2009), which has previously been employed in variety of bioscientific studies (Bassel et al., 2011; Glaab et al., 2010; Habashy et al., 2011).

The primary drivers behind this decision were the performance of existing machine learning algorithm implementations such as decision trees and the need to adapt them to include the importance score calculation.

In the present study, we have not reported training times since models have been trained with GP implementations coded in different programming languages (subsection Machine learning analyses and implementation).

Classification of essential genes was done using the Orange machine learning package's implementation of naïve Bayes.

A simple and area-efficient differential Gaussian circuit is presented for machine learning dedicated hardware implementations, where Gaussian functions are needed, e.g. for artificial neural networks (as transfer function), support vector machines (as kernel function) and fuzzy logic (as membership function).

The objective of the paper is to study the implementation of machine learning based controller to control non-linear systems.