Sentence examples for machine learning knowledge from inspiring English sources

Comprehend is trying to offer a way to build customized models without having any machine learning knowledge whatsoever.

Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge.

Bos [31] indicates machine learning, knowledge resources, and scaling inference as topics that can have a big impact on computational semantics in the future.

The machine learning knowledge extraction mechanisms used, i.e., point-wise mutual information (PMI), clustering, frequent itemset mining (FIM) do not have the depth to address the discussed issues and limitations.

In particular, Mitola anticipated that incorporation of substantial artificial intelligence (AI) in the form of machine learning, knowledge reasoning, and natural language processing will transform SDRs into cognitive radios that will optimize network performance by sensing, learning, and reacting to environmental stimuli[56].

This process finds practical application in fields such as machine learning, knowledge management, as well as data and text mining.

From the prediction perspective we expect improved performance and from the SAR analysis viewpoint it will be interesting to visualise how the expert hypotheses are blended into the machine learned knowledge.

This framework has been tested against thirty data sets, some very difficult, and generally as produced robust results; this has been achieved without any need of users thorough understanding of data, computer programming and/or machine-learning knowledge and complex parameterization to customize the complex modeling algorithms and procedures.

Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within "Big Data".

Nicoletti et al. [8] presented the following application examples: creation of machine learning methods, knowledge representation, inductive reasoning, data mining, processing of imperfect or incomplete information, pattern recognition, and discovery of knowledge in databases.

The goal of this paper is to present Computer Go by showing the links between existing studies on Computer Go and different AI related domains: evaluation function, heuristic search, machine learning, automatic knowledge generation, mathematical morphology and cognitive science.