15.2Machine learning Machine learningis a sub-area of artificial intelligence that is finding exciting applications inimage processing; it enables computer algorithms to learn from input/training data [265,266]. Application ofmachine learning methodsto large databases is called data mining [267]. Lear...
You are encouraged to provide a graphical abstract at submission. The graphical abstract should summarize the contents of your article in a concise, pictorial form which is designed to capture the attention of a wide readership. A graphical abstract will help draw more attention to your online art...
Artificial intelligence (AI), particularly,machine learning (ML)have grown rapidly in recent years in the context of data analysis and computing that typically allows the applications to function in an intelligent manner [95]. ML usually provides systems with the ability to learn and enhance from ...
neonrvm - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings. cONNXr - An ONNX runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run infe...
Awesome Machine Learning A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php. If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. Also, a listed repository should be deprecat...
Abstract Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using mod...
Abstract Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been ...
高斯过程，可看Rasmussen 的Gaussian processes for machine learning。第七章，最大margin，也就是SVM，...
Abstract Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Extracting ...
Abstract This review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal role of machine learning in shaping the development and integration of digital twin technology across multiple disciplines. Emphasising key ...