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Sparse coding has been popularly used as an effective data represen-tation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional s...
Automatically classifying the tissues types of Region of Interest (ROI) in med-ical imaging has been an important application in Computer-Aided Diagno-sis (CAD), such as classification of breast paren...
We present a two-stage approach for learning dic-tionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, d...
Wireless sensor networks are composed of dis-tributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and de...
A new procedure, calledDDα-procedure, is developed to solve the problem of classifyingd-dimensional objects into q≥2 classes. The pro-cedure is completely nonparametric; it usesq-dimensional depth plo...
We consider probabilistic multinomial probit classification using Gaussian process (GP) priors. The challenges with the multiclass GP classification are the integration over the non-Gaussian posterior...
Machine learning approaches to multi-label document classification have (to date) largely relied on discriminative modeling techniques such as support vector machines. A drawback of these approaches i...
A series of ten plant species belonging to Magnoliopsida - Dicotyledons class were analyzed in terms of chemical compounds distribution of abundance, starting from the assumption that these distributi...
Recent surge of interests in cognitive assessment has led to the developments of novel statistical models for diagnostic classification. Central to many such models is the well-known Q-matrix, which s...
In this article, we study rates of convergence of the generalization error of multi-class margin classifiers. In particular, we develop an upper bound theory quantifying the generalization error of va...
We consider the classification problem on the cube $[0,1]^d$ when the Bayes rule is known to belong to some new functions classes. These classes are made of prediction rules satisfying some conditions...
Let $(X,Y)$ be a random variable consisting of an observed feature vector $X in XX$ and an unobserved class label $Y in {1,2,ldots,L}$ with unknown joint distribution. In addition, let $DD$ be a train...

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