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Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input...
The standard support vector machine (SVM) is celebrated for its theoretically guaranteed generalization performance. However, it lacks sparsity and thus cannot be used for feature selection. Zero norm SVM is ideal in the sense of sparsity while its optimization is prohibitive due to the combinatorial nature of zero norm. In this paper, 1 norm and infinite norm constraints are employed simultaneously...
Multiple instance learning (MIL) has received increasing amount of research interest in machine learning recent years for its wide applications in image classification, text categorization, computer security, etc. Unlike supervised learning, in MIL, only the labels of bags are known, the instance labels in positive bags are not available. Many algorithms make the assumption that the instances in the...
Support vector machine(SVM) is based on the minimum of structure risk and used for small samples in machine learning. Memory support vector machine(MSVM) feedback is based on SVM and used cumulation samples replacing feedback samples by memory. It reduces the risk of recall vibration. MSVM feedback also proposes memory label which is used for lightening user's burden. MSVM feedback is proved its superiority...
Brain activation detection is an important problem in fMRI data analysis. In this paper, we propose a data-driven activation detection method called neighborhood one-class SVM (NOC-SVM). By incorporating the idea of neighborhood consistency into one-class SVM, the method classifies a voxel as an activated or non-activated voxel by its neighbor weighted distance to a hyperplane in a high- dimensional...
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