Machine learning (ML) modeling by DNA computing

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...

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Problems solved by technology

However, such regularization of the training data is a computationally intensive process

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