Add to ORKGThe ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through ...
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With h...
Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable i...
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology ...
This paper proposes an ontology and a markup language to describe machine learning experiments in a ...
Research in machine learning and data mining can be speeded up tremendously by moving empirical rese...
Explainable Machine Learning comprises methods and techniques that enable users to better understand...
Despite recent efforts to achieve a high level of interoperability of Machine Learning (ML) experime...
Ontologies are increasingly being used to provide background knowledge in machine learning models. W...
Scientific image data sets can be continuously enriched by labels describing new features which are ...
In this poster, we introduce how the FAIR¹ Digital Object (FAIR DO) concept can simplif...
Machine learning (ML) teams often work on a project just to realize the performance of the model is ...
Day by day, machine learning is changing our lives in ways we could not have imagined just 5 years a...
Machine learning (ML) practitioners and organizations are building model repositories of pre-trained...
We propose a taxonomy for classifying and describing papers which contribute to making Machine Learn...
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With h...
Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable i...
The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology ...
This paper proposes an ontology and a markup language to describe machine learning experiments in a ...
Research in machine learning and data mining can be speeded up tremendously by moving empirical rese...
Explainable Machine Learning comprises methods and techniques that enable users to better understand...
Despite recent efforts to achieve a high level of interoperability of Machine Learning (ML) experime...
Ontologies are increasingly being used to provide background knowledge in machine learning models. W...
Scientific image data sets can be continuously enriched by labels describing new features which are ...
In this poster, we introduce how the FAIR¹ Digital Object (FAIR DO) concept can simplif...
Machine learning (ML) teams often work on a project just to realize the performance of the model is ...
Day by day, machine learning is changing our lives in ways we could not have imagined just 5 years a...
Machine learning (ML) practitioners and organizations are building model repositories of pre-trained...
We propose a taxonomy for classifying and describing papers which contribute to making Machine Learn...
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by...
'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With h...
Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable i...