Support Vector Machine (SVM) is used for many machine learning tasks such as pattern recognition, object classification, and regression analysis in the case of the time series prediction.
Kernel methods have become an increasingly popular tool for machine learning tasks such as classification, regression or novelty detection.
However, computing kNN joins on vast amounts of data can be very time consuming when conducted by a single CPU, as it requires computing kNN for each element in dataset R. Additionally, a possible extension of such methods to perform machine learning tasks such as classification or regression, magnifies the complexity.
Particularly, the use of programmable quantum processors has been investigated to address machine learning tasks such as pattern matching [4], binary classification [5 8], feedback-adaptive quantum measurements [9], learning of unitary transformations [10], 'probably approximately correct' learning [11], and unsupervised clustering [12].
Industrial robots are reliable machines for manufacturing tasks such as welding, panting, assembly, palletizing or kitting operations.
Roboticists are making halting progress in developing machines for specific tasks, such as decontaminating and removing melted nuclear fuel masses, but they know that their creations need to be adaptable.
The farmers used the machines for menial offline tasks such as bookkeeping and printing out harvesting permits and pay stubs.
Digital Genius from Disrupt NY 2015 uses natural language processing to let machines take on mundane tasks, such as customer service and automated onboarding to services.
They regularly visit the vending machines to perform several tasks such as checking the fill levels, re-fill if required, maintenance, as well as repairing damages.
Recently, WR techniques have been widely used to improve various machine learning-based NLP tasks, such as part-of-speech (POS), chunking, and NER in newswire domain [ 20], and entity recognition in clinical text [ 33– 33].
