Results 291 to 300 of about 1,295,192 (344)

Large margin strategies in machine learning

2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2002
Controlling the capacity of a learning system in a way that does not depend on the dimensionality of the hypothesis space provides the key for effectively using large neural networks and decision trees, ensemble methods and kernel-induced feature spaces.
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Human Action Recognition using Machine Learning in Uncontrolled Environment

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), 2021
Video based Human Action Recognition (HAR) is an active research field of Machine Learning (ML) and human detection in videos is the most important step in action recognition. Recently, several techniques and algorithms have been proposed to increase the
Inzamam Mashood Nasir, M. Raza, J. H. Shah, Muhammad Attique Khan, A. Rehman   +4 more
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Voltage stability margin prediction by ensemble based extreme learning machine

2013 IEEE Power & Energy Society General Meeting, 2013
Voltage stability margin (VSM) evaluation is one of the essential tasks of power system voltage stability analysis. Conventional methods for VSM calculation is based on continuation-power flow technique. Recently, there is growing interest to apply artificial neural network (ANN) techniques to rapidly predict the VSM.
null Rui Zhang, null Yan Xu, null Zhao Yang Dong, null Pei Zhang, null Kit Po Wong   +4 more
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Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds

IEEE\/OSA Journal of Optical Communications and Networking, 2021
By associating machine learning and an analytical model (i.e., the Gaussian noise model), we reduce uncertainties on the output power profile and the noise figure of each amplifier in an optical network. We leverage the signal-to-noise ratio (SNR) of all
E. Seve, J. Pesic, Y. Pointurier
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Comparison of Conventional Gadoxetate Disodium-Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion.

AJR. American journal of roentgenology, 2021
OBJECTIVE. This study aimed to determine the best model for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using conventional gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (gadoxetate disodium)-enhanced MRI ...
Yidi Chen, Yuwei Xia, P. Tolat, L. Long, Zijian Jiang, Zhong-Kui Huang, Qin Tang   +6 more
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Increasing the energy efficiency of a data center based on machine learning

Journal of Industrial Ecology, 2021
Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs
Zhen Yang, Jin-Hong Du, Yiting Lin, Zhen Du, Li Xia, Qianchuan Zhao, Xiaohong Guan   +6 more
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Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data

Data and Metadata
Diabetes is a chronic disease that affects millions of people worldwide. Early diagnosis and effective management are crucial for reducing its complications.
Najah Al-shanableh, M. Alzyoud, Raya Alhusban, Nail M. Alshanableh, Ashraf Al-Oun, M. Al-batah, Salem Alzboon Mowafaq   +6 more
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Using Active Learning to Develop Machine Learning Models for Reaction Yield Prediction

Molecular Informatics, 2021
Computer aided synthesis planning, suggesting synthetic routes for molecules of interest, is a rapidly growing field. The machine learning methods used are often dependent on access to large datasets for training, but finite experimental budgets limit ...
Hampus Gummesson Svensson, Simon Johansson, E. Bjerrum, Alexander Schliep, M. Chehreghani, C. Tyrchan, O. Engkvist   +6 more
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Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

IEEE Transactions on Neural Networks and Learning Systems, 2019
Positive and unlabeled learning (PU learning) aims to train a binary classifier based on only PU data. Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem. However, none of them fully take the data distribution information into consideration when designing the model, which hinders them from ...
Chen Gong, Tongliang Liu, Jian Yang, Dacheng Tao   +3 more
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Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach

, 2020
We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. We select our model input based on existing accounting theories,
Yang Bao, B. Ke, Bin Li, Y. Yu, Jie Zhang   +4 more
semanticscholar   +1 more source