Sentence examples for machine decision from inspiring English sources

By combining image and diagnostic parameters, the machine decision diagnosis auxiliary algorithm is established.

For the baseline method, extraversion and leadership classification models were constructed using support vector machine, decision forest and ridge regression classifiers.

For example, support vector machine, decision tree and artificial neural network (ANN) are widely used to detect instability of power systems by using post-fault dynamic data during a few cycles [11, 12, 13].

Various multivariate statistical methods such as principal component analysis, support vector machine, decision tree and random forest were developed and tested their ability to discriminate the healthy and active TB samples.

CBD's generalization accuracy is compared with that of C4.5, C4.5 with rules, incremental decision trees, oblique classifiers, linear machine decision trees, CN2, learning vector quantization (LVQ), backpropagation, nearest neighbor, Q* and radial basis functions (RBFs).

We extracted a rich set of features from each file and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate.

In a companion article in Science magazine, the Harvard psychologist Joshua D. Greene suggested that the thorniest challenges in machine decision-making may be "more philosophical than technical.

Wang Hongbo, chief investment officer at CMB International Capital says: "With algorithm and AI, Tianrang lowers the requirement of complex machine decision-making and makes it accessible and scalable for commercial use".

In this study, data information of 2,789,675 patients in three hospitals in China are collected, compiled, and used as the research basis; these data are obtained through image acquisition and diagnostic parameter machine decision-making method on the basis of the machine diagnosis and medical system design model of adjuvant therapy.

It supports attribute inspections and overruling of machine decisions.

Current technology, however, still warrants human supervision in coping with (data) uncertainties and consequently in judging the validity of machine decisions.