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Machine learning for active matter
Nature Machine Intelligence, 2020The availability of large datasets has boosted the application of machine learning in many fields and is now starting to shape active-matter research as well. Machine learning techniques have already been successfully applied to active-matter data—for example, deep neural networks to analyse images and track objects, and recurrent nets and random ...
Frank Cichos +3 more
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Active Sampling for Learning Interpretable Surrogate Machine Learning Models
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020The use of machine learning methods to inform consequential decisions is increasingly expanding across many fields. As a result, the ability to interpret these models has become to a greater extent crucial to increase the related-technologies acceptance level and reliability. In this paper, we propose an active sampling approach for learning accurately
Amal Saadallah, Katharina Morik
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Machine Learning Classification Of Active
International Journal of Computing Algorithm, 2020Client turnover in the banking industry has grown according to the report. Churn can be classified into a variety of types. It s common knowledge that the cost of acquiring a new client is significantly greater than that of the expense of keeping an existing one. The objective is to find the most accurate machine learning-based churn prediction systems
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Sequential active learning using meta-cognitive extreme learning machine
Neurocomputing, 2016This paper proposes a fast and effective sequential active learning method using meta-cognitive extreme learning machine (SEAL-ELM). The proposed algorithm consists of two components, namely the cognitive component and the meta-cognitive component.
Yong Zhang, Meng Joo Er
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Machine learning activity detection using ML.Net
2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2020our living environment is becoming more and more aware of our presence and starts to react and interact with us. The smart house has long moved from concept to reality., as our homes are now digitalized with all sorts of smart devices. Since we now have more data available the ever., an important part is to be able to analyze the data and provide the ...
Anca Alexan +2 more
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Labor Activity Prediction Using Machine Learning
2022Abstract— In the realm of ubiquitous computing and context aware computing, labour activity detection is a hot issue. In this research, we present a labour activity detection approach in which accelerometer, gyroscope, and magnetometer data from wearable devices are gathered and utilised as input to a random forests (RF) model to categorise labour ...
Reenu Joseph, Nimmy Francis
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Active Machine Learning for Consideration Heuristics
Marketing Science, 2011We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a “configurator.” Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy).
Daria Dzyabura, John R. Hauser
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"Active Sourcing" mit Machine Learning
Viele Unternehmen versuchen durch Personalvermittler:innen oder selbst via LinkedIn aktiv geeignete Führungskräfte zu identifizieren und anzusprechen.Doch welche Qualifikationen und Profile rücken Personalvermittler:innen bei Bewerbungen in den Vordergrund, und welche die Bewerber:innen?Olbert-Bock, Sibylle +3 more
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Constructing machine learning potentials with active learning
2023Cheng Shang, Zhi-Pan Liu
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