392,958 research outputs found
Research and Analysis of Machine Learning Algorithm in Artificial Intelligence
ăAbstractăThis article firstly explains the concepts of artificial intelligence and algorithm separately, then determines the research status of artificial intelligence and machine learning in the background of the increasing popularity of artificial intelligence, and finally briefly describes the machine learning algorithm in the field of artificial intelligence, as well as puts forward appropriate development prospects, in order to provide theoretical reference for industry insider
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We show that universal consistency of
Empirical Risk Minimization remains possible using the MAPE instead of the MAE.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015,
Proceedings of the 23-th European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN 2015
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue âAdvances in Artificial Intelligence: Models, Optimization, and Machine Learningâ of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Editorial Note
I am delighted to introduce the 2nd issue of volume 2 of the International Journal of Automation, Artificial Intelligence and Machine Learning (IJAAIML). Artificial Intelligence (AI) and Machine Learning (ML) have applications in wide range of domains such as finance, national security, health care, criminal justice, transportation, and smart cities. With the focus on new ideas related to artificial intelligence and machine learning, this journal offers a platform to the authors in academia and industry to publish their novel research. It aims to serve the scientific community with brand new research publications to advance the research in AI and ML
Artificial Intelligence in Healthcare
Artificial intelligence is to reduce human cognitive functions. It is bringing an approach to healthcare, powdered by increasing the availability of healthcare data and rapid progress of analyst techniques. We can survey the current status of Artificial intelligence applications in healthcare and discuss its future uses. It is the most transformative technology of the 21th century. Healthcare has been identified as an early candidate to be revolutized by artificial intelligence technologies. This article aims for providing an early stage contribution with the decision making capacities of artificial intelligence technologies. The possible ethical and legally complex backdrop of the existing framework. I will conclude the present structures are largely fit to deal with the challenge of artificial intelligence are present will discuss clearly about the artificial intelligence contribution to the present health care. Artificial intelligence, machine learning, deep learning can assist with proactive patient care, reduced future risk and streamlined work processes.
Keywords: Artificial intelligence, machine learning, clinical decision support
Basic protocols in quantum reinforcement learning with superconducting circuits
Superconducting circuit technologies have recently achieved quantum protocols
involving closed feedback loops. Quantum artificial intelligence and quantum
machine learning are emerging fields inside quantum technologies which may
enable quantum devices to acquire information from the outer world and improve
themselves via a learning process. Here we propose the implementation of basic
protocols in quantum reinforcement learning, with superconducting circuits
employing feedback-loop control. We introduce diverse scenarios for
proof-of-principle experiments with state-of-the-art superconducting circuit
technologies and analyze their feasibility in presence of imperfections. The
field of quantum artificial intelligence implemented with superconducting
circuits paves the way for enhanced quantum control and quantum computation
protocols.Comment: Published versio
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