The predictive accuracy of 5 machine learning classifiers (logistic regression classifier, random forest classifier, support vector machine, k-nearest neighbor, and adaptive boosting) was examined ...

In a breakthrough for artificial intelligence (AI) and finance, computer scientists from Texas A&M University have developed a machine learning based method called Symbolic Modeling to handle ...

Of course, classification in machine learning can go well beyond sorting your inbox. Any kind of data, whether alphanumeric or visual, can be sorted by artificial intelligence.

Traditional quantum classifiers can theoretically leverage the advantages of quantum computing to accelerate machine learning tasks, but they still face numerous challenges in practical applications.

In the paper, the LLNL team describes applying deep reinforcement learning to discrete optimization — problems that deal with discrete “building blocks” that must be combined in a particular order or ...

The resulting classifier could identify tumor reactive T cells from TILs with 90% accuracy, works in many different types of tumor, and accommodates data from different cell sequencing technologies.

Machine learning classifier accelerates the development of cellular immunotherapies Date: March 15, 2024 Source: German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) Summary ...

Beyond performance, the framework breaks new ground in its unification of symbolic reasoning and statistical learning.