Machine learning (ML) modeling by DNA computing

A machine learning model, computer technology, applied in the field of computer program products and systems to generate machine learning models that are parallelized and regularized by DNA computations

Pending Publication Date: 2020-12-25
KYNDRYL INC
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, such regularization of the training

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  • Machine learning (ML) modeling by DNA computing
  • Machine learning (ML) modeling by DNA computing
  • Machine learning (ML) modeling by DNA computing

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Embodiment Construction

[0040] figure 1 A system 100 for machine learning (ML) modeling by DNA computing is depicted in accordance with one or more embodiments set forth herein.

[0041] DNA computing is a branch of computing that uses deoxyribonucleic acid (DNA), a linear strand of nucleotides with genetic instructions for growth, development, function and reproduction. Thus, DNA computing also exploits biochemical, DNA nanotechnology, and / or molecular biology hardware to describe and solve problems traditionally addressed by conventional silicon-based computer technology. DNA computing can be used in conjunction with conventional digital computer technology. With respect to highly parallel, high-speed computing, DNA computing is particularly advantageous because DNA computing exploits the aspect of DNA that many different molecules of DNA simultaneously form many DNA strands, corresponding to many different possibilities and / or solutions to the posed problem.

[0042] In the context of modeling ...

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Abstract

Methods, computer program products, and systems are presented. The methods include, for instance: identifying a training data set and defining a window for an initial beta value representing bias tolerated in formulating expectation conditional to each feature vector from the training data set. The conditional expectations are parallelly regularized by use of DNA computer. Amongst numerous combinations of candidate models, a best fit ensemble is produced as the machine learning model for predicting targeted outcomes based on inputs other than the training data set.

Description

technical field [0001] The present disclosure relates to machine learning techniques, and more particularly to methods, computer program products, and systems for generating machine learning models regularized by DNA computation in parallel. Background technique [0002] In traditional machine learning (ML) techniques, training data is often regularized to address the problem of overfitting the ML model to the training data, thereby making the ML model useful for input data different from the training data. However, such regularization of training data is a computationally intensive process. Also, since many regularization methods available today provide clear benefits in regularizing ML models, it is well known that highly regularized training data is essential for making ML models robust and reliable for a wide variety of input data. Contents of the invention [0003] By providing, on the one hand, a method, the disadvantages of the prior art are overcome and other adva...

Claims

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Application Information

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IPC IPC(8): G06N5/00
CPCG06N3/123G06N20/20G06N20/00
Inventor G.F.迪亚曼蒂A.鲍格曼M.马佐拉蒂
Owner KYNDRYL INC
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