Local learning integrating global structure for large scale semi-supervised classification

2012 ◽  
Author(s):  
Guangchao Wu ◽  
Yuhan Li ◽  
Jianqing Xi ◽  
Xiaowei Yang ◽  
Xiaolan Liu
Keyword(s):  
Supervised Classification ◽  
Large Scale ◽  
Global Structure ◽  
Local Learning

scholarly journals Local learning integrating global structure for large scale semi-supervised classification

2013 ◽  
Vol 66 (10) ◽  
pp. 1961-1970 ◽  
Author(s):  
Guangchao Wu ◽  
Yuhan Li ◽  
Xiaowei Yang ◽  
Jianqing Xi
Keyword(s):  
Supervised Classification ◽  
Large Scale ◽  
Global Structure ◽  
Local Learning

Global structure and dynamics of large-scale fluctuations in the solar wind: Voyager 2 observations during 2005 and 2006

2008 ◽  
Vol 113 (A2) ◽  
pp. n/a-n/a ◽  
Author(s):  
L. F. Burlaga ◽  
N. F Ness ◽  
M. H. Acũna ◽  
Y.-M. Wang ◽  
N. R. Sheeley ◽  
...  
Keyword(s):  
Solar Wind ◽  
Large Scale ◽  
Global Structure ◽  
Structure And Dynamics ◽  
Voyager 2

scholarly journals Nakamoto Consensus to Accelerate Supervised Classification Algorithms for Multiparty Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhen Zhang ◽  
Bing Guo ◽  
Yan Shen ◽  
Chengjie Li ◽  
Xinhua Suo ◽  
...  
Keyword(s):  
Distributed Computing ◽  
Supervised Classification ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Classification Algorithms ◽  
Distributed Data ◽  
Data Intensive ◽  
Private Data ◽  
Cooperation Mechanism ◽  
Mathematical Formulas

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals SYNERGISTIC USE OF SENTINEL-1 AND SENTINEL-2 TIME SERIES FOR POPLAR PLANTATIONS MONITORING AT LARGE SCALE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1457-1461
Author(s):  
Y. Hamrouni ◽  
É. Paillassa ◽  
V. Chéret ◽  
C. Monteil ◽  
D. Sheeren
Keyword(s):  
Time Series ◽  
Active Learning ◽  
Supervised Classification ◽  
Large Scale ◽  
Training Data ◽  
Passive Learning ◽  
Training Samples ◽  
Poplar Plantations ◽  
Annual Means ◽  
Sentinel 2

Abstract. The current context of availability of Earth Observation satellite data at high spatial and temporal resolutions makes it possible to map large areas. Although supervised classification is the most widely adopted approach, its performance is highly dependent on the availability and the quality of training data. However, gathering samples from field surveys or through photo interpretation is often expensive and time-consuming especially when the area to be classified is large. In this paper we propose the use of an active learning-based technique to address this issue by reducing the labelling effort required for supervised classification while increasing the generalisation capabilities of the classifier across space. Experiments were conducted to identify poplar plantations in three different sites in France using Sentinel-2 time series. In order to characterise the age of the identified poplar stands, temporal means of Sentinel-1 backscatter coefficients were computed. The results are promising and show the good capacities of the active learning-based approach to achieve similar performance (Poplar F-score ≥ 90%) to traditional passive learning (i.e. with random selection of samples) with up to 50% fewer training samples. Sentinel-1 annual means have demonstrated their potential to differentiate two stand ages with an overall accuracy of 83% regardless of the cultivar considered.

scholarly journals Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

2020 ◽  
Vol 34 (05) ◽  
pp. 8673-8680
Author(s):  
Pengda Qin ◽  
Xin Wang ◽  
Wenhu Chen ◽  
Chunyun Zhang ◽  
Weiran Xu ◽  
...  
Keyword(s):  
Supervised Classification ◽  
Large Scale ◽  
Relational Learning ◽  
Generative Adversarial Networks ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Performance Improvements ◽  
Adversarial Networks ◽  
Knowledge Graphs ◽  
Noisy Text

Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.

Detecting online expressional anomie and its evolutions in social media

2019 ◽  
Vol 37 (4) ◽  
pp. 703-721
Author(s):  
Qingqing Zhou ◽  
Ming Jing
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Supervised Classification ◽  
Large Scale ◽  
Local Conditions ◽  
Content Type ◽  
Fine Grained ◽  
Rapid Transmission ◽  
National Literacy ◽  
Microblog Mining

Purpose Expressional anomie (e.g. obscene words) can hinder communications and even obstruct improvements of national literacy. Meanwhile, the borderless and rapid transmission of the internet has exacerbated the influences. Hence, the purpose of this paper is detecting online anomic expression automatically and analyzing dynamic evolution processes of expressional anomie, so as to reveal multidimensional status of expressional anomie. Design/methodology/approach This paper conducted expressional anomie analysis via fine-grained microblog mining. Specifically, anomic microblogs and their anomic types were identified via a supervised classification method. Then, the evolutions of expressional anomie were analyzed, and impacts of users’ characteristics on the evolution process were mined. Finally, expressional anomie characteristics and evolution trends were obtained. Findings Empirical results on microblogs indicate that more effective and diversified measures need to be used to address the current large-scale anomie in expression. Moreover, measures should be tailored to individuals and local conditions. Originality/value To the best of the authors’ knowledge, it is the first research to mine evolutions of expressional anomie automatically in social media. It may discover more continuous and universal rules of expressional anomie, so as to optimize the online expression environment.

scholarly journals Decoupling graph convolutional networks for large-scale supervised classification

2020 ◽  
Vol 178 ◽  
pp. 337-344
Author(s):  
Mariia Koreneva ◽  
Alexander A. Visheratin ◽  
Denis Nasonov
Keyword(s):  
Supervised Classification ◽  
Large Scale ◽  
Convolutional Networks

scholarly journals Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap

2018 ◽  
Vol 10 (7) ◽  
pp. 1145 ◽  
Author(s):  
Yann Forget ◽  
Catherine Linard ◽  
Marius Gilbert
Keyword(s):  
High Resolution ◽  
Supervised Learning ◽  
Spatial Data ◽  
Urban Areas ◽  
Supervised Classification ◽  
Large Scale ◽  
Landsat Imagery ◽  
Training Samples ◽  
Wide Range ◽  
Sub Saharan

The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database.

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