0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Identification and classification of materials using machine vision and machine learning in the context of industry 4.0

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            The industrial management of SMEs in the era of Industry 4.0

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Comparing support vector machines with Gaussian kernels to radial basis function classifiers

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Intelligent Manufacturing
                J Intell Manuf
                Springer Science and Business Media LLC
                0956-5515
                1572-8145
                June 2020
                November 16 2019
                June 2020
                : 31
                : 5
                : 1229-1241
                Article
                10.1007/s10845-019-01508-6
                29d16b8b-1ed0-4e55-b467-1f3805633b46
                © 2020

                http://www.springer.com/tdm

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article