In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predicted from the input, so it is also critical to have a predictable label encodi...

We consider the problem of labeling dynamic (3D) maps: we develop real-time algorithms that attach non-overlapping annotations to objects on maps which the user can pan, zoom, and rotate continuously. Existing algorithms either label static maps or only a small number of the objects. We consider the problem to label streets and to label points. We label streets either internally or externally. ...

To capture the interdependencies between labels in multi-label classification problems, classifier chain (CC) tries to take the multiple labels of each instance into account under a deterministic high-order Markov Chain model. Since its performance is sensitive to the choice of label order, the key issue is how to determine the optimal label order for CC. In this work, we first generalize the C...

We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i.e., the zero-shot learning setting). We develop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for ea...

Multi-label learning deals with the problem that each instance is associated multiple labels simultaneously. Most of existing approaches aim to improve performance multi-label by exploiting label correlations. Although data augmentation technique widely used in many machine tasks, it still unclear whether helpful learning. In this article, we propose leverage Specifically, first a novel approac...

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models. The results reveal a number of surprising facts. In particular, under the noise model of Tsybakov (2004), the minimax label complexity of active learning with a VC class is always asymptotically smaller than that of passiv...

We discuss a method to improve the exact F-measure maximization algorithm called GFM, proposed in [2] for multi-label classification, assuming the label set can be partitioned into conditionally independent subsets given the input features. If the labels were all independent, the estimation of only m parameters (m denoting the number of labels) would suffice to derive Bayes-optimal predictions ...

MULTIPLE KERNEL AND MULTI-LABEL LEARNING FOR IMAGE CATEGORIZATION By Serhat Selçuk Bucak One crucial step in recovering useful information from large image collections is image categorization. The goal of image categorization is to find the relevant labels for a given image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there rema...

In classification, it is often difficult or expensive to obtain completely accurate and reliable labels. Indeed, labels may be polluted by label noise, due to e.g. insufficient information, expert mistakes, and encoding errors. The problem is that errors in training labels that are not properly handled may deteriorate the accuracy of subsequent predictions, among other effects. Many works have ...

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research communi...