This website introduces a new mathematical framework for two related classical problems in statistical learning: data clustering and data classification.
Benchmarks on the symbolic regression and data classification domains were performed.
In this interdisciplinary approach, a link is done between problem solving methods (operations research technics and data classification algorithms) and human machine interaction (solving control modes).
Deep Belief learning method recently is used in several state-of-the-art studies due to its potential in robustness computation, pattern recognition, and data classification.
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"This will serve to strengthen existing modeling and data classification engines and to refine the system's machine learning," it adds.
For data processing (building dictionaries and data classification), Spark is employed.
The company may also get the data encrypted by uploading it and then downloading it with the tags and data classification in place.
The K-Nearest-Neighbor (K-NN) classifier is one of the most basic classifiers for pattern recognition and data classification.
The idea of this system is to use the previous individual solutions such as clustering, data prioritization, and data classification in addition to efficient techniques for network management.
ANNs were widely employed in the last decades, and they are among the best solutions for pattern recognition and data classification problems.
