Examples of using Machine-learning approach in English and their translations into Chinese
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Machine-learning approach identifies plants at risk of extinction.
The new study overcomes this challenge by using a machine-learning approach. .
That's why certain machine-learning approaches are described with such terms as“neural networks.”.
Computer science researchers at one university are developing a machine-learning approach to fake news detection.
Machine-learning approach helps discover new ways of controlling spatial organization of induced pluripotent stem cells.
Thanks to these connections, I showed how numerous machine-learning approaches could also help in multiagent planning.
This machine-learning approach can also compensate for errors in a manner specific to the algorithm and hardware platform.
Doctors want an interface and medical records, images-each requires a different machine-learning approach,” he says.
A better machine-learning approach could be applied in just about any industry that handles large amounts of data, he says.
Scientists at the Sloan Kettering Institute have developed a machine-learning approach to building personalized cancer nanomedicines.
A machine-learning approach to assigning attribution values across the different channels of a successful conversion event.
In the new study,Collins and Yang decided to take a machine-learning approach to investigate how this happens and what the consequences are.
A novel machine-learning approach could lead to faster identification of the animal source of certain Salmonella outbreaks.
This innovative approach allowed researchers to both better evaluate intrusion detection systems andapply modern machine-learning approaches.
Using this machine-learning approach, the team could leverage large amounts of microRNA data and develop different predictive models.
S paper"Map-Reduce for Machine Learning on Multicore" buthas since evolved to cover much broader machine-learning approaches.
In this exciting new machine-learning approach, developed by a Google researcher, two neural networks are pitted against one another;
S paper"Map-Reduce for Machine Learning on Multicore"(see Related topics)but has since evolved to cover much broader machine-learning approaches.
Using this machine-learning approach, the team could leverage large amounts of microRNA data and develop different predictive models.
Of course, it takes an expert quantitative analyst to design the machine-learning approach, but one such analyst can ultimately generate millions of models over time.
Again, using a machine-learning approach, they identified miRNA expression ratios that were both predictive of diagnosis and uninfluenced by these variables.
In a new study of antibiotic action,MIT researchers developed a new machine-learning approach to discover an additional mechanism that helps some antibiotics kill bacteria.
Machine-learning approaches are being used to solve many problems, and this team used it to look for latent knowledge in the world of materials science.
But banks, the military, employers,and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable.
Unlike traditional and machine-learning approaches, which attempt to copy brain function, neuromorphic computing mimics the brain's structure.
But banks, the military, employers,and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable.
Capper et al.1 used a machine-learning approach to classify brain tumours on the basis of genome-wide patterns of a type of DNA alteration called methylation.
Machine-learning approaches have been used to filter and classify Kepler data before, but Shallue's neural network offered a far more powerful algorithm.
The researchers concluded that the machine-learning approach outperformed standard risk assessment systems and identified more low-risk patients who could safely be sent home.
Their work, based on the machine-learning approach of generative models, significantly advances the development of self-learning artificial intelligence, while also deepening understanding of human cognition.