Crisp Weighted Support Vector Regression for robust single model estimation: application to object tracking in image sequences
F Dufrenois, J Colliez, D Hamad - 2007 IEEE Conference on …, 2007 - ieeexplore.ieee.org
Support Vector Regression (SVR) is now a well-established method for estimating real-
valued functions. However, the standard SVR is not effective to deal with outliers and
structured outliers in training data sets commonly encountered in computer vision
applications. In this paper, we present a weighted version of SVM for regression. The
proposed approach introduces an adaptive binary function that allows a dominant model
from a degraded training dataset to be extracted. This binary function progressively …
valued functions. However, the standard SVR is not effective to deal with outliers and
structured outliers in training data sets commonly encountered in computer vision
applications. In this paper, we present a weighted version of SVM for regression. The
proposed approach introduces an adaptive binary function that allows a dominant model
from a degraded training dataset to be extracted. This binary function progressively …
Weighted support vector regression for robust single model estimation: Application to motion segmentation in image sequences
F Dufrenois, J Colliez, D Hamad - 2007 International Joint …, 2007 - ieeexplore.ieee.org
Support Vector Regression (SVR) is now a well-established method for estimating real-
valued functions. However, the standard SVR is not effective to deal with outliers and
structured outliers in training data sets commonly encoutered in computer vision
applications. In this paper, we present a weighted version of SVM for regression. The
proposed approach introduces an adaptive binary function that allows a dominant model
from a degraded training dataset to be extracted. This binary function progressively …
valued functions. However, the standard SVR is not effective to deal with outliers and
structured outliers in training data sets commonly encoutered in computer vision
applications. In this paper, we present a weighted version of SVM for regression. The
proposed approach introduces an adaptive binary function that allows a dominant model
from a degraded training dataset to be extracted. This binary function progressively …
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