Yongxin Taylor Xi

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By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifiers. We prove that the proposed algorithm exhibits a "(More)
Boosting algorithms with l 1-regularization are of interest because l 1 regularization leads to sparser composite classifiers. Moreover, Rosset et al. have shown that for separable data, standard l p-regularized loss minimization results in a margin maximizing classifier in the limit as regulariza-tion is relaxed. For the case p = 1, we extend these results(More)
The Support Vector Machine (SVM) methodology is an effective , supervised, machine learning method that gives state-of-the-art performance for brain state classification from functional magnetic resonance brain images (fMRI). Due to the poor scalability of SVM (cubic in the number of training points) and the massive size of fMRI images, a SVM analysis is(More)
This paper introduces KPCatcher (keyphrase catcher). The value of our work lies in providing concrete solutions to building a real keyphrase extraction product for enterprise videos. KPCatcher has been designed to robustly extract a ranked list of keyphrases from enterprise videos, independent of the domain. It treats noun phrases in the transcript as(More)
Real-time functional magnetic resonance imaging (rtfMRI) enables classification of brain activity during data collection thus making inference results accessible to both the subject and experimenter during the experiment. The major challenge of rtfMRI is the potential loss of inference accuracy due to the resource limitations that rtfMRI imposes. For(More)
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