Results 251 to 260 of about 8,272,458 (291)

Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning.

Health Technol Assess
Uthman OA, Court R, Enderby J, Al-Khudairy L, Nduka C, Mistry H, Melendez-Torres GJ, Taylor-Phillips S, Clarke A.   +8 more
europepmc   +1 more source

Automated Machine Learning [PDF]

The Springer Series on Challenges in Machine Learning, 2019
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.
Wenwu Zhu, Xin Wang
exaly   +10 more sources

Machine Learning in Automated Text Categorization [PDF]

ACM Computing Surveys, 2002
The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a ...
Fabrizio Sebastiani
exaly   +6 more sources

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Automated Machine Learning on Graph

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Machine learning on graphs has been extensively studiedin both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks.
Xin Wang 0019, Wenwu Zhu 0001
openaire   +1 more source

Automated Feature Engineering for Automated Machine Learning

Knowledge-Based Systems
Automated feature engineering (AutoFE) and automated machine learning (AutoML) can both be used in a machine learning project to improve the efficiency of a data scientist. In recent years, different algorithms have been developed for both sub fields independently of each other.
Flavius Frasincar, Casper de Winter, Vladyslav Matsiiako   +2 more
exaly   +3 more sources

iSklearn: Automated Machine Learning with irace

2021 IEEE Congress on Evolutionary Computation (CEC), 2021
Automated algorithm engineering has become an important asset for academia and industry. irace, for instance, is an algorithm configurator (AC) that has successfully designed effective algorithms for optimization problems. The major advantage of irace is combining learning and parallelization, but no fully-functional automated machine learning (AutoML)
Carlos Vieira, Adelson de Araújo, José E. Andrade, Leonardo C. T. Bezerra   +3 more
openaire   +2 more sources

Machine Learning In Engineering Automation

1991
Abstract Engineers need intelligent tools to assist them with problems such as design, planning, monitoring, control, diagnosis, and analysis. Manual construction of these tools can be costly or impossible due to problems such as large amounts of data, lack of problem understanding, and the expense of knowledge engineering.
Steve A. Chien, Bradley L. Whitehall, Thomas G. Dietterich, Richard J. Doyle, Brian Falkenhainer, James Garrett, Stephen C. Y. Lu   +6 more
openaire   +2 more sources

Role of Machine Learning in ETL Automation

Proceedings of the 21st International Conference on Distributed Computing and Networking, 2020
In the current business landscape, real-time analysis of enterprise data is very crucial for decision-makers of the organization to take strategic resolution and stay ahead of the competitors. Most of the time, it happens that data is outdated by the time it reaches to the user.
Kartick Chandra Mondal, Neepa Biswas, Swati Saha   +2 more
openaire   +2 more sources

Automated Machine Learning in the Wild

Proceedings of the 10th ACM Conference on Recommender Systems, 2016
Machine Learning research is progressing at an ever increasing pace. Fueled by technology advances commonly referred to as "Big Data", all data related fields are teaming with scientific and applied activity: our communities explore new application areas, develop new learning algorithms, and continuously scale and improve optimization and estimation ...
openaire   +2 more sources

A machine learning algorithm for automated assembly

Proceedings. 1991 IEEE International Conference on Robotics and Automation, 2002
A primary source of difficulty in automated assembly is the uncertainty in the relative position of the parts being assembled. This study focuses on a machine learning approach for solving the problem. Force sensor information, responses to recent moves and results from previous assemblies are used to generate a set of production rules.
Erik Vaaler, Warren P. Seering
openaire   +2 more sources