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» Robust feature induction for support vector machines
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ICASSP
2011
IEEE
12 years 9 months ago
Fall detection in a smart room by using a fuzzy one class support vector machine and imperfect training data
In this paper, we propose an efficient and robust fall detection system by using a fuzzy one class support vector machine based on video information. Two cameras are used to capt...
Miao Yu, Syed Mohsen Naqvi, Adel Rhuma, Jonathon A...
ICCV
2007
IEEE
13 years 11 months ago
Co-Tracking Using Semi-Supervised Support Vector Machines
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
ICCV
2001
IEEE
14 years 7 months ago
Face Recognition with Support Vector Machines: Global versus Component-based Approach
We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first...
Bernd Heisele, Purdy Ho, Tomaso Poggio
BMCBI
2004
114views more  BMCBI 2004»
13 years 5 months ago
Profiled support vector machines for antisense oligonucleotide efficacy prediction
Background: This paper presents the use of Support Vector Machines (SVMs) for prediction and analysis of antisense oligonucleotide (AO) efficacy. The collected database comprises ...
Gustavo Camps-Valls, Alistair M. Chalk, Antonio J....
3DPVT
2006
IEEE
197views Visualization» more  3DPVT 2006»
13 years 9 months ago
Aerial LiDAR Data Classification Using Support Vector Machines (SVM)
We classify 3D aerial LiDAR scattered height data into buildings, trees, roads, and grass using the Support Vector Machine (SVM) algorithm. To do so we use five features: height, ...
Suresh K. Lodha, Edward J. Kreps, David P. Helmbol...