Not exact matches
Even though the research studied 30,000 cases, «there are still a lot of things that need to be looked at,» he said, «but based on our research and what our testing has shown, we are able to
predict the future success of a new campaign that the
machine -
learning algorithm has never seen before with up to 80 per cent accuracy.»
Then, to narrow the field, a team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), led by Ryan Adams, Assistant Professor of Computer Science, developed new
machine learning algorithms to
predict which molecules were likely to have good outcomes, and prioritize those to be virtually tested.
Machine learning algorithms can now reliably diagnose skin cancers (from photographs) and lung cancer, and
predict the risk of seizures.
By analyzing tweets and air quality information together, Ram and her collaborators were able to use
machine learning algorithms to
predict with 75 percent accuracy whether the emergency room could expect a low, medium or high number of asthma - related visits on a given day.
Zafar and colleagues tested their technique by designing a crime -
predicting machine -
learning algorithm with specific nondiscrimination instructions.
Machine learning algorithms can
predict failure times of laboratory quakes with remarkable accuracy.
In a follow - up study, the researchers were able to examine the word associations of a group of 324 participants and, by using
machine -
learning algorithms,
predict their political leanings and what party they belonged to, as well as which candidate they were likely to vote for in the presidential elections.
Lawrence Livermore National Laboratory researchers Priyadip Ray (left) and Brenden Petersen and their teams, using
machine learning algorithms, have developed computer models that can more accurately characterize a patient's progression through stages of sepsis and better
predict mortality risk by integrating past medical history, real - time vital signs and other diagnostics.
In a first for
machine -
learning algorithms, a new piece of software developed at Caltech can
predict behavior of bacteria by reading the content of a gene.
The method is the first to
predict the outcomes of a major international court by automatically analysing case text using a
machine learning algorithm.
But using
machine learning algorithms to classify and
predict document relevancy can be applied to any document set.
Implemented the Support Vector
Machine Learning algorithm to
predict energy consumption for a household