18457601. INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT simplified abstract (KABUSHIKI KAISHA TOSHIBA)

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INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT

Organization Name

KABUSHIKI KAISHA TOSHIBA

Inventor(s)

Takeichiro Nishikawa of Yokohama (JP)

Gen Li of Kawasaki (JP)

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT - A simplified explanation of the abstract

This abstract first appeared for US patent application 18457601 titled 'INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND COMPUTER PROGRAM PRODUCT

Simplified Explanation

An information processing device is designed to learn a machine learning model by setting an error function based on weights and features of elements, and then using this function to improve the model's accuracy.

  • The device sets an error function based on weights and features of elements.
  • The error function is used during the learning process of a machine learning model.
  • The model is trained using the error function to improve its accuracy.

Key Features and Innovation

  • Setting an error function based on weights and features of elements.
  • Using the error function to train a machine learning model.
  • Improving the accuracy of the model through the learning process.

Potential Applications

This technology can be applied in various fields such as:

  • Predictive analytics
  • Image recognition
  • Natural language processing

Problems Solved

  • Enhances the accuracy of machine learning models.
  • Improves the efficiency of training processes.
  • Enables better decision-making based on data analysis.

Benefits

  • Increased accuracy in predictive modeling.
  • Faster training of machine learning models.
  • Enhanced performance in data analysis tasks.

Commercial Applications

Title: Enhanced Machine Learning Model Training for Improved Predictive Analytics This technology can be utilized in industries such as:

  • Healthcare for disease diagnosis
  • Finance for fraud detection
  • Marketing for customer segmentation

Prior Art

Readers can explore prior art related to machine learning model training and error functions in the field of artificial intelligence and data science.

Frequently Updated Research

Stay updated on the latest advancements in machine learning model training and error function optimization to enhance predictive analytics and data analysis.

Questions about Machine Learning Model Training

How does setting an error function based on weights and features improve machine learning model training?

Setting an error function helps the model understand the discrepancies between predicted and actual outcomes, guiding it towards better accuracy.

What are the potential challenges in using error functions for machine learning model training?

Challenges may include selecting the right features and weights, as well as ensuring the error function accurately reflects the model's performance.


Original Abstract Submitted

According to an embodiment, an information processing device includes one or more hardware processors configured to: set an error function including one or more terms based on a plurality of weights according to features of a plurality of elements, the error function being a function used during learning of a machine learning model into which positions of a plurality of atoms included in an analysis target, and information indicating which of the plurality of elements the plurality of atoms are, are input, and that outputs a physical quantity of the analysis target; and learn the machine learning model using the error function.

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