Sentence examples for machine learning environment from inspiring English sources

For all the above feature selection techniques we used the implementation provided by the WEKA machine learning environment [ 31].

We give preference to CFMask for operational cloud and cloud shadow detection, as it is derived from a priori knowledge of physical phenomena and is operable without geographic restriction, making it useful for current and future land imaging missions without having to be retrained in a machine-learning environment.

Machine learning, virtual environments, and systems integration concepts have obviously been around for years.

The NB and HNB modules from the popular open source machine learning software, Waikato Environment for Knowledge Analysis (Weka) [ 14], were employed for the two-step Bayes classification framework.

The article presented an overview of applications of machine learning for building knowledge on environment characteristics (context) and user preferences.

Evolv embeds sensors in the environment, applies machine learning and artificial intelligence to help us understand what's happening.

IBM wants to provide data scientists with the same types of machine learning capabilities in a mainframe environment that they are used to finding in the cloud.

We discussed the Hadoop ecosystem and a number of tools that are a part of it in order to provide context to how machine learning fits into an analytics environment.

Table 1 Challenges and requirements for CR-VANETs Challenges Required mechanisms Reliable delivery of safety messages QoS support Highly dynamic topologies Algorithms with fast   convergence time Scarce spectrum Efficient use of spectrum Newer and several technologies Reprogrammability Varied environment and Machine learning algorithms spectrum availability  .

The company also unveiled a partnership with artificial intelligent researchers Deepmind to collaborate on opening the real-time strategy game "StarCraft 2" as a testing environment for machine learning.

Metalearning can help to reduce the amount of experimentation by providing dynamic advice in form of assistants, decrease the time that has to be spent on introducing, tuning and maintaining models and help to promote machine learning outside of an academic environment.