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Feature selection with specific multivariate performance measures is the key to the success of many applications such as image retrieval and text classification. The existing feature selection methods are usually designed for classification error. In this paper, we propose a generalized sparse regularizer. Based on the proposed regularizer, we present a(More)
Trefoil factor 3 (TFF3) is a member of the TFF-domain peptide family and essential in regulating cell migration and maintaining mucosal integrity in gastrointestinal tract. However, the role of TFF3 and its downstream regulating mechanisms in cancer cell migration remain unclear. We previously reported that TFF3 prolonged the up-regulation of Twist protein(More)
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervised kernel learning method which can seamlessly combine man-ifold structure of unlabeled data and Regularized Least-Squares (RLS) to learn a new kernel. Interestingly , the new(More)
In the absence of the labeled samples in a domain referred to as target domain, Domain Adaptation (DA) techniques come in handy. Generally, DA techniques assume there are available source domains that share similar predictive function with the target domain. Two core challenges of DA typically arise, variance that exists between source and target domains,(More)
Conditional random fields (CRF) and structural support vector machines (structural SVM) are two state-of-the-art methods for structured prediction that captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on all input(More)
Automatic image annotation, which is usually formulated as a multi-label classification problem, is one of the major tools used to enhance the semantic understanding of web images. Many multimedia applications (e.g., tag-based image retrieval) can greatly benefit from image annotation. However, the insufficient performance of image annotation methods(More)
We propose an adaptive ensemble method to adapt coreference resolution across domains. This method has three features: (1) it can optimize for any user-specified objective measure ; (2) it can make document-specific prediction rather than rely on a fixed base model or a fixed set of base models; (3) it can automatically adjust the active ensemble members(More)
Vascular and microvascular anastomoses are critical components of reconstructive microsurgery, vascular surgery, and transplant surgery. Intraoperative surgical guidance using a surgical imaging modality that provides an in-depth view and three-dimensional (3-D) imaging can potentially improve outcome following both conventional and innovative anastomosis(More)
Multiple kernel learning (MKL) and classifier ensemble are two mainstream methods for solving learning problems in which some sets of features/views are more informative than others, or the features/views within a given set are inconsistent. In this paper, we first present a novel probabilistic interpretation of MKL such that maximum entropy discrimination(More)
This paper is an initial investigation into using knowledge-based parameters in the field of statistical parametric speech synthesis (SPSS). Utilizing the types of speech parameters used in the Klatt Formant Synthesizer we present automatic techniques for deriving such parameters from a speech database and building a statistical parametric speech(More)