Results 271 to 280 of about 9,169,616 (322)

Sequential bootstrapped support vector machines a SVM accelerator

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., 2006
Support vector machine has obtained much success in machine learning. But it requires to solve a quadratic optimization (QP) problem so that its training time increases dramatically with the increase of training set. Hence, standard SVM with batch learning has difficulty in handling large scale problems.
Xuchun Li, Yan Zhu, Eric Sung
openaire   +2 more sources

Support vector machines (SVMs) for monitoring network design

Groundwater, 2005
Abstract In this paper we present a hydrologic application of a new statistical learning methodology called support vector machines (SVMs). SVMs are based on minimization of a bound on the generalized error (risk) model, rather than just the mean square error over a training set.
Asefa, T. M., Kemblowski, M., Urroz, G., McKee, Mac   +3 more
openaire   +3 more sources

Fusing binary support vector machines (SVM) into multiclass SVM

SPIE Proceedings, 2006
Multi-class support vector machine by fusing a class of binary support vector machines is proposed. The classifier fusion approaches include simple combination method such as Maximum, Minimum, Product, Mean, Median and Major Voting. Dempster-Shafer fusion method is also presented as well as KNN and Neural network approaches.
Zilu Ying, Jingwen Li, Youwei Zhang
openaire   +1 more source

Rolling bearing fault diagnosis with combined convolutional neural networks and support vector machine

, 2021
For small sample data, it is difficult to complete the requirements of training complex models in the field of fault diagnosis. To solve the problem, this paper combines convolutional neural network's excellent feature processing ability with the ...
Tian Han, Longwen Zhang, Z. Yin, A. Tan
semanticscholar   +1 more source

Deep feature based rice leaf disease identification using support vector machine

Computers and Electronics in Agriculture, 2020
Features are the vital factor for image classification in the field of machine learning. The advancement of deep convolutional neural network (CNN) shows the way for identification of rice diseases using deep features with the expectation of high returns.
Dr. Prabira Kumar Sethy, N. Barpanda, A. Rath, S. Behera   +3 more
semanticscholar   +1 more source

Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR)

Knowledge and Information Systems, 2007
A parallel randomized support vector machine (PRSVM) and a parallel randomized support vector regression (PRSVR) algorithm based on a randomized sampling technique are proposed in this paper. The proposed PRSVM and PRSVR have four major advantages over previous methods.
Yumao Lu, Vwani P. Roychowdhury
openaire   +1 more source

Support Vector Machines (SVM)

2018
In statistical learning theory (regression, classification, etc.) there are many regression models, such as algebraic polynomials,
Joseph L. Awange, Béla Paláncz, Robert H. Lewis, Lajos Völgyesi   +3 more
openaire   +1 more source

EuDiC SVM: A novel support vector machine classification algorithm

Intelligent Data Analysis, 2016
The Support Vector Machine (SVM) is a powerful technique for data classification. For linearly separable data points, the SVM constructs an optimal separating hyper-plane as a decision surface, to divide the data points of different categories in the vector space.
Hetal Bhavsar, Amit Ganatra
openaire   +1 more source

Image recognition of four rice leaf diseases based on deep learning and support vector machine

Computers and Electronics in Agriculture, 2020
In the field of agricultural information, identification and prediction of rice leaf diseases has always been a research focus. Deep learning and support vector machine (SVM) technology are hot research topics in the field of pattern recognition at ...
F. Jiang, Yang Lu, Yu Chen, Di Cai, Gong-Fa Li   +4 more
semanticscholar   +1 more source

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   +2 more sources