Partial label learning (PLL) aims to learn from inexact data annotations where each training example is associated with a coarse candidate set. Due its practicability, many PLL algorithms have been proposed in recent literature. Most prior works attempt identify the ground-truth labels sets and classifier trained afterward by fitting features of examples their exact labels. From different persp...

Partial multi-label learning (PML), which tackles the problem of prediction models from instances with overcomplete noisy annotations, has recently started gaining attention research community. In this paper, we propose a novel adversarial model, PML-GAN, under generalized encoder-decoder framework for partial learning. The PML-GAN model uses disambiguation network to identify irrelevant labels...

To facilitate the XML query processing, several kinds of labeling schemes have been proposed. Based on the labeling schemes, the ancestordescendant and parent-child relationships in XML queries can be quickly determined without accessing the original XML file. Recently, more researches are focused on how to update the labels when nodes are inserted into the XML. However how to process the delet...

There is a huge demand to be able to find and integrate heterogeneous data sources, which requires mapping the attributes of a source to the concepts and relationships defined in a domain ontology. In this paper, we present a new approach to find these mappings, which we call semantic labeling. Previous approaches map each data value individually, typically by learning a model based on features...

Valuable information can be hidden in images, however, few research discuss data mining on them. In this paper, we propose a general framework based on the decision tree for mining and processing image data. Pixel-wised image features were extracted and transformed into a database-like table which allows various data mining algorithms to make explorations on it. Each tuple of the transformed ta...

Information network mining often requires examination of linkage relationships between nodes for analysis. Recently, network representation has emerged to represent each node in a vector format, embedding network structure, so off-the-shelf machine learning methods can be directly applied for analysis. To date, existing methods only focus on one aspect of node information and cannot leverage no...

With increasing information density and complexity, computer displays may become visually cluttered, adversely affecting overall usability. Text labels can significantly add to visual clutter in graphical user interfaces, but are generally kept legible through specific label placement algorithms that seek visual separation of labels and other objects in the 2D view plane. This work studies an a...

This work addresses the problem of optimally solving Markov Random Fields(MRFs) in which labels obey a certain topology constraint. Utilizing prior information, such as domain knowledge about the appearance, shape, or spatial configuration of objects in a scene can greatly improve the accuracy of segmentation algorithms in the presence of noise, clutter, and occlusion. Nowhere is this more evid...

Theorists of criminal law widely agree that state punishment involves harsh treatment and stigma states must therefore provide protections for targeted individuals. But certain regulatory measures can also be used to impose stigma. This paper addresses the stigmatic impact measures. It argues label individuals as risky a has potential significantly affect these individual’s personal professiona...

Retailers’ brands maker with private label have significantly boosted market share in recent years. Creating new brands for goods or services provide differentiation with similar distributors. The main aim of this paper is to test which component can be more effective in consumers’ purchase intention based on using private label for goods’ image. This research data was collected by prior st...