Results 241 to 250 of about 303,008 (292)

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

Diseases classification using support vector machine (SVM)

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02., 2004
The paper proposed a new method: disease classification based on protein sequence. Support vector machine was used for this problem and a new encoding for the multicode protein sequence was suggested. Two extracted features were selected for classifying, the results showed the capability of SVM for such bioinformatics problems and the goodness of the ...
null Liu Sheng, null Song Qing, null Hu Wenjie, null Cao Aize   +3 more
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

MS-SVM: Minimally Spanned Support Vector Machine

Applied Soft Computing, 2018
Abstract For a Support Vector Machine (SVM) algorithm, the time required for classifying an unknown data point is proportional to the number of support vectors. For some real time applications, use of SVM could be a problem if the number of support vectors is high.
Rupan Panja, Nikhil R. Pal
openaire   +1 more source

Support Vector Machines (SVMs)

2015
This Chapter details a class of learning mechanisms known as the Support Vector Machines (SVMs). We start by giving the machine learning framework, define and introduce the concepts of linear classifiers, and describe formally the SVMs as large margin classifiers. We focus on the convex optimization problem and in particular we deal with the Sequential
Noel Lopes, Bernardete Ribeiro
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

Construction Legal Decision Support Using Support Vector Machine (SVM)

Construction Research Congress 2010, 2010
This paper represents a step in a line of research aiming at mitigating the negative effects of conflicts on the construction industry through developing a construction legal decision support methodology. This step developed Support Vector Machine (SVM) models to automatically extract latent legal factors, upon which judges base their verdicts, from ...
Tarek Mahfouz, Amr Kandil
openaire   +1 more source

�������������� ������������������ ������������������ ���� ���������������������� Support Vector Machines (SVM)

2008
The tools for forecasting economic failures, allowing managers to take timely strategic action so that the economic risk can be avoided. They enable accountants to assist in the adoption of their reports and other external participants to identify the financial risks and take appropriate decisions.
openaire   +1 more source

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