17526617. CASCADING META LEARNER TO ENHANCE FUNCTIONALITIES OF MACHINE LEARNING MODELS simplified abstract (Capital One Services, LLC)

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CASCADING META LEARNER TO ENHANCE FUNCTIONALITIES OF MACHINE LEARNING MODELS

Organization Name

Capital One Services, LLC

Inventor(s)

Anh Truong of Champaign IL (US)

Austin Walters of Savoy IL (US)

Jeremy Goodsitt of Champaign IL (US)

CASCADING META LEARNER TO ENHANCE FUNCTIONALITIES OF MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17526617 titled 'CASCADING META LEARNER TO ENHANCE FUNCTIONALITIES OF MACHINE LEARNING MODELS

Simplified Explanation

The patent application describes a system that enhances the functionalities of machine learning models by cascading a meta learner. Here are the key points:

  • The system includes a cascading server that converts a first machine learning model into a neural network machine learning model.
  • An embedding machine learning model is used by the cascading server to extract metadata from a set of data records.
  • The cascading server generates a first set of feature vectors using the converted neural network machine learning model, and a second set of feature vectors using the embedding machine learning model.
  • These feature vectors are then concatenated to generate concatenated feature vectors.
  • The cascading server trains a combined machine learning model using the concatenated feature vectors.
  • The trained combined machine learning model enhances the performance of the first machine learning model.

Potential Applications

  • This technology can be applied in various fields where machine learning models are used, such as healthcare, finance, and marketing.
  • It can be used to improve the accuracy and performance of existing machine learning models.

Problems Solved

  • The system solves the problem of limited functionality and performance of individual machine learning models.
  • It addresses the challenge of extracting meaningful metadata from data records to enhance the performance of machine learning models.

Benefits

  • The cascading meta learner improves the functionalities and performance of machine learning models.
  • By combining different types of feature vectors, the system can leverage the strengths of multiple models and improve overall accuracy.
  • The system provides a flexible and scalable approach to enhance machine learning models without the need for significant modifications to the original models.


Original Abstract Submitted

Systems as described herein may cascade a meta learner to enhance functionalities of machine learning models. A cascading server may convert a first machine learning model to a neural network machine learning model. The cascading server may use an embedding machine learning model to extract metadata from a plurality of data records. The cascading server may generate a first set of feature vectors using the converted neural network machine learning model, and generate a second set of feature vectors using the embedding machine learning model. The cascading sever may concatenate the first set of feature vectors with the second set of feature vectors to generate concatenated feature vectors. Accordingly, the cascading server may train a combined machine learning model using the concatenated feature vectors and the trained combined machine learning model may enhance performance of the first machine learning model.

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