17643242. METALEARNER FOR UNSUPERVISED AUTOMATED MACHINE LEARNING simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

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METALEARNER FOR UNSUPERVISED AUTOMATED MACHINE LEARNING

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Saket Sathe of Mohegan Lake NY (US)

Long Vu of Chappaqua NY (US)

Peter Daniel Kirchner of Putnam Valley NY (US)

Charu C. Aggarwal of Yorktown Heights NY (US)

METALEARNER FOR UNSUPERVISED AUTOMATED MACHINE LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 17643242 titled 'METALEARNER FOR UNSUPERVISED AUTOMATED MACHINE LEARNING

Simplified Explanation

The patent application describes a method, system, and computer program for a metalearner that automates machine learning. Here are the key points:

  • The method starts by receiving a labeled data set.
  • It then generates a set of data subsets from the labeled data set.
  • Next, a set of unsupervised machine learning pipelines is generated.
  • A training set is created using the set of data subsets and unsupervised machine learning pipelines.
  • The method then trains a metalearner specifically designed for unsupervised tasks using the training set.

Potential applications of this technology:

  • Automated machine learning: This innovation can be used to automate the process of machine learning, making it easier and more efficient for users.
  • Data analysis: The metalearner can be applied to analyze large datasets and extract valuable insights without the need for manual intervention.
  • Pattern recognition: By training the metalearner on unsupervised tasks, it can be used to identify patterns and trends in data.

Problems solved by this technology:

  • Time-consuming manual processes: The method automates the machine learning process, reducing the time and effort required for users to train models.
  • Complex data analysis: The metalearner simplifies the analysis of large and complex datasets by automatically generating subsets and pipelines.
  • Lack of expertise: Users without extensive knowledge of machine learning can still benefit from this technology as it automates the process.

Benefits of this technology:

  • Efficiency: By automating machine learning, the method saves time and resources for users.
  • Accuracy: The metalearner is trained on a diverse set of data subsets and pipelines, leading to more accurate predictions and analysis.
  • Accessibility: Users with limited machine learning expertise can still leverage this technology to analyze data and gain insights.


Original Abstract Submitted

A method, system, and computer program product for a metalearner for automated machine learning are provided. The method receives a labeled data set. A set of data subsets is generated from the labeled data set. A set of unsupervised machine learning pipelines is generated. A training set is generated from the set of data subsets and the set of unsupervised machine learning pipelines. The method trains a metalearner for unsupervised tasks based on the training set.

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