Results 11 to 20 of about 1,480,093 (371)

Practical challenges in data‐driven interpolation: Dealing with noise, enforcing stability, and computing realizations

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary In this contribution, we propose a detailed study of interpolation‐based data‐driven methods that are of relevance in the model reduction and also in the systems and control communities. The data are given by samples of the transfer function of the underlying (unknown) model, that is, we analyze frequency‐response data.
Quirin Aumann, Ion Victor Gosea
wiley   +1 more source

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source

Selection of Specialization Class Using Support Vector Machine (SVM) Method in Sekolah Menengah Atas Negeri 1 Ambon

Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021
The curriculum is a plan to form the abilities and character of children based on a standard. One of its form is the division of specialization classes at the high school level.
Stevanny Tamaela, Yopi Andry Lesnussa, Venn Yan Ishak Ilwaru   +2 more
doaj   +1 more source

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

Diagnostics, 2023
Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease.
Abdul Aziz K Abdul Hamid, Wan Imanul Aisyah Wan Mohamad Nawi, Muhamad Safiih Lola, Wan Azani Mustafa, Siti Madhihah Abdul Malik, Syerrina Zakaria, Elayaraja Aruchunan, Nurul Hila Zainuddin, R.U. Gobithaasan, Mohd Tajuddin Abdullah   +9 more
doaj   +1 more source

Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image ...
M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, Pedram Ghamisi, Saeid Homayouni   +5 more
semanticscholar   +1 more source

Overcome Support Vector Machine Diagnosis Overfitting

Cancer Informatics, 2014
Support vector machines (SVMs) are widely employed in molecular diagnosis of disease for their efficiency and robustness. However, there is no previous research to analyze their overfitting in high-dimensional omics data based disease diagnosis, which is
Henry Han, Xiaoqian Jiang
doaj   +2 more sources

Two-Phase Indefinite Kernel Support Vector Machine

Jisuanji kexue yu tansuo, 2020
Recently, indefinite kernel support vector machine (IKSVM) has attracted great attention in the machine learning community as more and more indefinite metric kernel matrices have occurred.
SHI Na, XUE Hui, WANG Yunyun
doaj   +1 more source

Support vector machine for functional data classification [PDF]

, 2005
In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them.
Aronszajn, Besse, Biau, Cardot, Cardot, Cristianini, Dauxois, Deville, Evgeniou, Fabrice Rossi, Ferré, Francois, Frank, Hastie, Hastie, Hastie, Hastie, Hoerl, James, Leurgans, Lin, Mallat, Marx, Nathalie Villa, Pezzulli, Ramsay, Ramsay, Rossi, Rossi, Rossi, Rossi, Sandberg, Sandberg, Smola, Smola, Steinwart, Steinwart, Stinchcombe, Vapnik, Vert, Villa   +40 more
core   +7 more sources

Support Vector Machines inR [PDF]

Journal of Statistical Software, 2006
Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations.
Karatzoglou, Alexandros, Meyer, David, Hornik, Kurt   +2 more
openaire   +9 more sources

MRI brain classification using support vector machine [PDF]

, 2011
The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Other than that, medical image retrieval system is to provide a tool for radiologists to retrieve the images ...
Abdullah, N. B., Kamal, N. F. B., Othman, Mohd. Fauzi   +2 more
core   +1 more source