Results 271 to 280 of about 2,307,608 (312)

Active Sampling for Learning Interpretable Surrogate Machine Learning Models

2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020
The 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, 2020
Client 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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Machine learning activity detection using ML.Net

2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), 2020
our 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, Alexandru Alexan, Oniga Stefan   +2 more
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Passive and active phase change materials integrated building energy systems with advanced machine-learning based climate-adaptive designs, intelligent operations, uncertainty-based analysis and optimisations: A state-of-the-art review

, 2020
Integrating phase change materials (PCMs) in buildings cannot only enhance the energy performance, but also improve the renewable utilization efficiency through considerable latent heat during charging/discharging cycles. However, system performances are
Yuekuan Zhou, Siqian Zheng, Zhengxuan Liu, T. Wen, Zhixiong Ding, Jun Yan, Guo-qiang Zhang   +6 more
semanticscholar   +1 more source

Labor Activity Prediction Using Machine Learning

2022
Abstract— 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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Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews
The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior.
M. Kulichenko, B. Nebgen, N. Lubbers, Justin S. Smith, Kipton Barros, Alice E. A. Allen, Adela Habib, Emily Shinkle, Nikita Fedik, Y. Li, Richard A. Messerly, Sergei Tretiak   +11 more
semanticscholar   +1 more source

Active learning-assisted directed evolution

bioRxiv
Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior.
Jason Yang, Ravi G. Lal, James C. Bowden, Raul Astudillo, Mikhail A. Hameedi, Sukhvinder Kaur, Matthew Hill, Yisong Yue, Frances H. Arnold   +8 more
semanticscholar   +1 more source

Active Machine Learning for Consideration Heuristics

Marketing Science, 2011
We 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 Learning for Neural Machine Translation

International Conference on Asian Language Processing, 2018
Neural machine translation (NMT) normally requires a large bilingual corpus to train a high-translation-quality model. However, building such parallel corpora for many low-resource language pairs is rather expensive.
Pei Zhang, Xueying Xu, Deyi Xiong
semanticscholar   +1 more source

"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, Wüest, Bruno, Meier, Claude, Weeber, Franziska   +3 more
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