As past years we participated to the high-level feature extraction task and we pursued on the fusion of classifier outputs. New for this year is an experiment with a twodimensional Hidden Markov Model. Altogether we submitted seven runs. Three runs was based on the SVM model, another three was based on the HMM and one run was done by fusing HMM results with the SVM. To compile the runs, color a...

 A_CL1_1: choose the best-performing classifier for each concept from all the following runs and an event detection method.  A_CL2_2: (visual-based) choose the best-performing visual-based classifier for each concept from runs A_CL4_4, A_CL5_5, and A_CL6_6.  A_CL3_3: (visual-text) weighted average fusion of visual-based classifier A_CL4_4 with a text classifier.  A_CL4_4: (visual-based) ave...

Compared with labeled data, unlabeled data are significantly easier to obtain. Currently, classification of unlabeled data is an open issue. In this paper a novel SVMKNN classification methodology based on Semi-supervised learning is proposed, we consider the problem of using a large number of unlabeled data to boost performance of the classifier when only a small set of labeled examples is ava...

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer c...

To prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. Due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the Hughes phenomenon. A practical way to handle the Hughes problem is preparing a lot of training samples until the size ...

In this paper, the problem of implementing the reject option in support vector machines (SVMs) is addressed. We started by observing that methods proposed so far simply apply a reject threshold to the outputs of a trained SVM. We then showed that, under the framework of the structural risk minimisation principle, the rejection region must be determined during the training phase of a classifier....