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aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.12 from group net.exoego (version 0.26.1-v2.524.0)

aws-sdk-scalajs-facade-machinelearning-nodejs-v8

Group: net.exoego Artifact: aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.12
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Artifact aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.12
Group net.exoego
Version 0.26.1-v2.524.0
Last update 07. October 2019
Organization net.exoego
URL https://github.com/exoego/aws-sdk-scalajs-facade
License APL2
Dependencies amount 4
Dependencies scala-library, aws-sdk-scalajs-facade-core-nodejs-v8_sjs0.6_2.12, scalajs-library_2.12, scala-js-nodejs-v8_sjs0.6_2.12,
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aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.13 from group net.exoego (version 0.26.1-v2.524.0)

aws-sdk-scalajs-facade-machinelearning-nodejs-v8

Group: net.exoego Artifact: aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.13
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Artifact aws-sdk-scalajs-facade-machinelearning-nodejs-v8_sjs0.6_2.13
Group net.exoego
Version 0.26.1-v2.524.0
Last update 07. October 2019
Organization net.exoego
URL https://github.com/exoego/aws-sdk-scalajs-facade
License APL2
Dependencies amount 4
Dependencies scala-library, aws-sdk-scalajs-facade-core-nodejs-v8_sjs0.6_2.13, scalajs-library_2.13, scala-js-nodejs-v8_sjs0.6_2.13,
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azure-mgmt-machinelearning from group com.microsoft.azure (version 1.10.0-beta)

This package contains Microsoft MachineLearning Management SDK.

Group: com.microsoft.azure Artifact: azure-mgmt-machinelearning
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Artifact azure-mgmt-machinelearning
Group com.microsoft.azure
Version 1.10.0-beta
Last update 07. May 2018
Organization not specified
URL https://github.com/Azure/azure-sdk-for-java
License The MIT License (MIT)
Dependencies amount 2
Dependencies azure-client-runtime, azure-mgmt-resources,
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SPegasos from group nz.ac.waikato.cms.weka (version 1.0.2)

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

Group: nz.ac.waikato.cms.weka Artifact: SPegasos
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1 downloads
Artifact SPegasos
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/SPegasos
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
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