Results 171 to 180 of about 142,623 (210)

Face pose discrimination using support vector machines (SVM)

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 2002
This paper describes an approach for the problem of face pose discrimination using support vector machines (SVM). Face pose discrimination means that one can label the face image as one of several known poses. Face images are drawn from the standard FERET database. The training set consists of 150 images equally distributed among frontal, approximately
Jeffrey Huang, Xuhui Shao, Harry Wechsler   +2 more
openaire   +1 more source

TV-SVM: Support Vector Machine with Total Variational Regularization

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
It is required that input features are represented as vectors or scalars in machine learning for classification, e.g. support vector machine (SVM). However, real world data such as 2D images is naturally represented as matrices or tensors with higher dimensions.
Zhendong Zhang 0001, Cheolkon Jung
openaire   +1 more source

Diagnosis Using Support Vector Machines (SVM)

2016
Diagnosis of functional failures at the board level is critical for improving product yield and reducing manufacturing cost. State-of-the-art board-level diagnostic software is unable to cope with high complexity and ever-increasing clock frequencies, and the identification of the root cause of failure on a board is a major problem today.
Fangming Ye, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu   +3 more
openaire   +1 more source

Target recognition in SAR images with Support Vector Machines (SVM)

2007 IEEE International Geoscience and Remote Sensing Symposium, 2007
This paper addresses object recognition problem in SAR images with SVM classifier; the work has been mainly focused on feature vector definition. Actually, each object is represented by a feature vector and SVM aims to estimate the best hyperplanes that separate classes in the feature space.
Céline Tison, Nadine Pourthié, Jean-Claude Souyris   +2 more
openaire   +1 more source

Hardware Support Vector Machine (SVM) for satellite on-board applications

2014 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2014
Since their introduction in 1995, Support Vector Machines (SVM) have shown that classification by this relatively recent machine learning tool can be more accurate than popular contemporary techniques such as neural networks and decision trees, hence causing it to find its way quickly to various applications in engineering, economy and statistics ...
Abdul-Halim M. Jallad, Lubna B. Mohammed
openaire   +1 more source

SN-SVM: a sparse nonparametric support vector machine classifier

Signal, Image and Video Processing, 2012
This paper introduces a novel sparse nonparametric support vector machine classifier (SN-SVM) which combines data distribution information from two state-of-the-art kernel-based classifiers, namely, the kernel support vector machine (KSVM) and the kernel nonparametric discriminant (KND).
Naimul Mefraz Khan, Riadh Ksantini, Imran Shafiq Ahmad, Ling Guan   +3 more
openaire   +1 more source

Vote Parallel SVM: An Extension of Parallel Support Vector Machine

2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018
Support Vector Machine (SVM) is a set of machine learning algorithms, which has been widely used in diverse domains. With the increasing size of datasets, the traditional SVM training algorithms for large-scale datasets become infeasible. Mathematical optimization and cascade parallelism are both popular strategies for accelerating SVM training.
Yan Song, Qun Jin, Ke Yan 0001, Huijuan Lu, Julong Pan   +4 more
openaire   +1 more source

Support Vector Machines (SVM)

2023
Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari, T. S. Murugesh   +3 more
openaire   +2 more sources

Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)

International Journal of River Basin Management, 2019
The current calculations of water quality index (WQI) were sometimes can be very complex and time-consuming which involves sub-index calculation like BOD and COD, however with the support vector ma...
Wei Cong Leong, Alireza Bahadori, Jie Zhang, Z. Ahmad   +3 more
openaire   +2 more sources

Asymptotic efficiency of kernel support vector machines (SVM)

Cybernetics and Systems Analysis, 2009
The paper analyzes the asymptotic properties of Vapnik's SVM-estimates of a regression function as the size of the training sample tends to infinity. The estimation problem is considered as infinite-dimensional minimization of a regularized empirical risk functional in a reproducing kernel Hilbert space.
V. I. Norkin, M. A. Keyzer
openaire   +1 more source