Sentence examples for multiple machine learning from inspiring English sources
The system, from researchers at Google's DeepMind, made incredibly sophisticated use of multiple machine learning techniques to achieve this feat.
Similarly, Herrera-Yagüe and Zufiria applied multiple machine learning algorithms on 22 features incorporating node-level activity, ego-network strucuture and homophily to predict age and gender [45].
Based on the above considerations, this study aimed to compare the performance of multiple machine learning methods for classifying mediastinal lymph node NSCLC from PET/CT images.
By measuring cultural alignment utilising multiple machine learning components, Bunch provides a solution that helps companies select candidates that fit the team, shape their culture early on when hiring and drive its success," she says.
Commenting in a statement, Tobias Jahn, principal at BMW i Ventures, said: "The versatility of Graphcore's IPU – which supports multiple machine learning techniques with high efficiency – is well-suited for a wide variety of applications from intelligent voice assistants to self-driving vehicles.
In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors.
Our approach, which has focused on integration of multiple features through machine learning techniques, suits well to inclusion of multiple physiologic signals.
To exhaust the statistical aspects of model development, multiple and complementary machine learning algorithms should be made available.
When the same atom is identified for multiple motifs, supervised machine learning techniques such as K-nearest neighbors are used to remove duplicates and assign the atom to the correct motif.
Recent years, deep ELM models like Hierarchical ELM (H-ELM), deep representations learning via ELM (Dr-ELM) have been proposed to be applied in multiple applications in machine learning.
On the other hand, PCM models are trained on both biological and chemical data for multiple targets by various machine learning techniques.
