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TNN
2010
159views Management» more  TNN 2010»
14 years 6 months ago
Multiple incremental decremental learning of support vector machines
We propose a multiple incremental decremental algorithm of Support Vector Machine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently w...
Masayuki Karasuyama, Ichiro Takeuchi
KDD
2003
ACM
129views Data Mining» more  KDD 2003»
16 years 4 days ago
Online novelty detection on temporal sequences
: Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework...
Junshui Ma, Simon Perkins
ICCV
2001
IEEE
16 years 1 months ago
Shape Deformation: SVM Regression and Application to Medical Image Segmentation
This paper presents a novel landmark-based shape deformation method. This method effectively solves two problems inherent in landmark-based shape deformation: (a) identification o...
Song Wang, Weiyu Zhu, Zhi-Pei Liang
BMCBI
2006
201views more  BMCBI 2006»
14 years 11 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
ESWA
2006
122views more  ESWA 2006»
14 years 11 months ago
Transmembrane segments prediction and understanding using support vector machine and decision tree
In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite...
Jieyue He, Hae-Jin Hu, Robert W. Harrison, Phang C...