Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 39

ML is a subset of AI. The aim of ML is to develop algorithms that can learn from data and solve specific problems in some context as human do [4]. ML has been proving its ability to overcome the challenges and complexities of mathematical formulation and solution of complex problems, including wired and wireless networking problems that require effective methods to quickly respond to dynamical changes of channels as well as the increasing diversification of services. Dynamic ML algorithms are able to process data and learn from it. They are replacement of complex algorithms which are written in a fixed way to conduct specific tasks.

The basic concept of ML is through training data that is used as input to the learning algorithm. The learning algorithm then produces a new set of rules, based on inferences from data, which results in a new algorithm. The new algorithm is officially referred to as the ML model. Traditional algorithms are comprised of a set of pre-programmed instructions used by the processor in the operation and management of a system. However, instructions of ML algorithms are formed based on real-life data acquired from the system environment. Thus, a machine is fed a large amount of data, it will analyze and classify data, then use the gained experience to improve its own algorithm and process data in a better way in the future. The strength of ML algorithms lies in their ability to infer new instructions or policies from data. The more data is available for the learning algorithms during the training phase, the more ML algorithms will be able to carry out their tasks efficiently and with greater accuracy.