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Linear SVMs are efficient in both training and testing, however the data in real applications is rarely linearly separable. Non-linear kernel SVMs are too computationally intensive for applications with large-scale data sets. Recently locally linear classifiers have gained popularity due to their efficiency whilst remaining competitive with kernel methods. The vanilla nearest neighbor algorithm is...
In this paper, we propose a novel image similarity learning approach based on Probabilistic Feature Matching (PFM).We consider the matching process as the bipartite graph matching problem, and define the image similarity as the inner product of the feature similarities and their corresponding matching probabilities, which are learned by optimizing a quadratic formulation. Further, we prove that the...
In this paper, we propose a novel large-margin based approach for multiple kernel learning (MKL) using biconvex optimization, called Adaptive Multiple Kernel Learning (AdaMKL). To learn the weights for support vectors and the kernel coefficients, AdaMKL minimizes the objective function alternately by learning one component while fixing the other at a time, and in this way only one convex formulation...
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