نتایج جستجو برای: multi label data
تعداد نتایج: 2803845 فیلتر نتایج به سال:
Multi-label classification is a central problem in many application domains. In this paper, we present a novel supervised bi-directional model that learns a low-dimensional mid-level representation for multilabel classification. Unlike traditional multi-label learning methods which identify intermediate representations from either the input space or the output space but not both, the mid-level ...
let $g=(v, e)$ be a graph with $p$ vertices and $q$ edges. an emph{acyclic graphoidal cover} of $g$ is a collection $psi$ of paths in $g$ which are internally-disjoint and cover each edge of the graph exactly once. let $f: vrightarrow {1, 2, ldots, p}$ be a bijective labeling of the vertices of $g$. let $uparrow!g_f$ be the directed graph obtained by orienting the...
This paper presents an overview of the ImageCLEF 2016 evaluation campaign, an event that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs 2016. ImageCLEF is an ongoing initiative that promotes the evaluation of technologies for annotation, indexing and retrieval for providing information access to collections of images in various usage scenarios and domains. ...
In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result is obtaining much faster classification and higher F-score. We also achieve substantial reduction of the model size.
The WISE 2014 challenge was concerned with the task of multi-label classification of articles coming from Greek print media. Raw data comes from the scanning of print media, article segmentation, and optical character segmentation, and therefore is quite noisy. Each article is examined by a human annotator and categorized to one or more of the topics being monitored. Topics range from specific ...
Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China 3 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {liuc,...
Multi-label classification deals with problems in which each instance can be associated with a set of labels. An effective multi-label method, named RAkEL, randomly breaks the initial set of labels into smaller sets and trains a single-label classifier in each of this subset. To classify an unseen instance, the predictions of all classifiers are combined using a voting process. In this paper, w...
This paper discusses multi-hop operation of an optical-label switching system, demonstrating rapid alloptical packet switching with 2R regeneration for the data and optical label swapping for the label. Introduction Optical-label switching technology has the potential to provide low latency and transparency desired for the next generation Internet [1-2]. For network applications, the router mus...
A large family of graph-based semi-supervised algorithms have been developed intuitively and pragmatically for the multi-label learning problem. These methods, however, only implicitly exploited the label correlation, as either part of graph weight or an additional constraint, to improve overall classification performance. Despite their seemingly quite different formulations, we show that all e...
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