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Extreme Large Margin Distribution Machine and its applications for biomedical datasets

Extreme Large Margin Distribution Machine and its applicatio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhiyong Yang Jingcheng Lu Taohong Zhang Department of Computer University of Science and Technology Beijing (USTB) Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China

ISBN: (纸本)9781509016129

Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin theory show that, besides maximizing the minimum margin of a given training dataset, it is also necessary to optimization the margin distribution to boost the overall generalization ability. Correspondingly, a novel SVM-like algorithm called Large Margin Distribution Machine (LDM) realizes this idea by maximizing the average of margin and minimizing the variance of margin simultaneously. And a series of applications has been reported thereafter. There is another well-known machine learning algorithm called Extreme Learning Machine (ELM) which shares similar framework with SVM. It is believed in this paper ELM could also benefit from the virtues of margin distribution optimization. Bearing this in mind, a novel algorithm called Extreme Large Margin Distribution Machine(ELDM) is proposed in this paper by bridging the advantages of ELM and LDM. And an efficient extension of ELDM for multi-class classifications under One vs. All Scheme is proposed subsequently. Finally, the experiment results on both benchmark datasets and biomedical classification datasets show the effectiveness of our proposed algorithm.

关键词： Heart Glass Training Support vector machines Algorithm design and analysis Diabetes Kernel

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Women empowerment towards developing India

Women empowerment towards developing India

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： A. Priyadharshini R. Thiyagarajan V. Kumar T. Radhu Department of Computer Science and Engineering Knowledge Institute of Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Shreenivasa Engineering College Dharmapuri Tamil Nadu India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem Tamil Nadu India

ISBN: (纸本)9781509041787

India is a developing country and the citizens of India are dreaming that within 2020, India will become a well-developed nation. Indian government has started Start-up India, Make in India to improve our nation, but still women in India are facing lots of obstacle towards their improvement. Despite this, women are having high exposure and they are working in all fields like defense, engineering, medical, etc. In order to make all women in the nation to attain a standard Women Empowerment is needed. Women Empowerment focuses on empowering each and every woman in the country to make them independent in all aspects in the society, to be aware on the rights and to make them equip about physical security. This paper focuses on describing the issues that women are facing in their daily life, schemes available for Women Empowerment in India and Self Help Group which is successfully running in the state of Tamil Nadu, suggestions on Self Help Group for future enhancement and a case study of Women Empowerment Cell.

关键词： Government Training Industries Economic indicators Monitoring

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Erratum to: The use of data-derived label hierarchies in multi-label classification

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Journal of Intelligent Information Systems 2016年第2期48卷 475-476页

作者： Gjorgji Madjarov Dejan Gjorgjevikj Ivica Dimitrovski Sašo Džeroski Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

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Curating a Semantic Bibliographic Catalog

Curating a Semantic Bibliographic Catalog

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International Conference on Semantic Computing

作者： H. S. Bhargav Gangadhar Akalwadi K. Kishan Kavi Mahesh Department of Computer Science and Engineering PES Institute of Technology Bangalore India KAnOE - Centre for Knowledge Analytics and Ontological Engineering PES University Bangalore India

Large online libraries are now common across the Web. However these libraries are usually fragmented and are not semantically connected thereby making their search and access difficult. Also there are no large bibliographic Linked Open Datasets available for use in research and analytics. This paper shows how to create a large, comprehensive, RDF triple store of semantic data about books. The primary aim of this work is to establish a linked relation between all the available books in the world and connect them to the already available linked datasets. The Open Library dataset, which has over 45 million records is first serialized by converting it into a triple format and then linked together using predicates from different ontologies. A simple endpoint for a semantic book search engine called BookLOD is created to demonstrate the utility of the dataset.

关键词： Semantics Libraries Resource description framework Ontologies Vocabulary Search engines

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Information Diffusion Model and Hadoop Implementation Research on Online Social Network

Information Diffusion Model and Hadoop Implementation Resear...

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2016 International Conference on computer, Mechatronics and Electronic engineering (CMEE 2016)

作者： Hong-song CHEN Yue HU Department of Computer Science University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Institute of information engineering Chinese Academy of Sciences University of Chinese Academy of Sciences

Social networks are currently gotten increasing focus with the growth of online social network services, such as Facebook, Sina Weibo. One major challenge for social network research is how to build information diffusion model to describe the complex information diffusion model. For this purpose, information diffusion models are researched. To show the importance of network topology, how to identify the most important spreaders in the network is a key scientific problem. Different kinds of network centrality measure methods are analyzed. The role of a potential edge in social network is analyzed. The relationship between network topology structure and information diffusion dynamics model is analyzed. Pagerank algorithm for Sina Weibo users are implemented in Hadoop cloud computing platform. Experiment result shows that Pagerank algorithm can be implemented in Hadoop cloud platform to recognize the important spreaders in social network. Big data in social network can be analyzed in the platform.

关键词： Social network Information diffusion model Hadoop Network topology

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on computer, Communication and Computational sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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Evolving Type-2 Recurrent Fuzzy Neural Network

Evolving Type-2 Recurrent Fuzzy Neural Network

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International Joint Conference on Neural Networks

作者： Mahardhika Pratama Edwin Lughofer Meng Joo Er Wenny Rahayu Tharam Dillon Department of Computer Science and IT La Trobe University Department of knowledge-based mathematical system Johannes Kepler University School of Electrical and Electronic Engineering Nanyang Technological University

ISBN: (纸本)9781509006212

Evolving intelligent system (EIS) is a machine learning algorithm, specifically designed to deal with learning from large data streams. Although the EIS research topic has attracted various contributions over the past decade, the issue of uncertainty, temporal system dynamic, and system order are relatively unexplored by existing studies. A novel EIS, namely evolving type-2 recurrent fuzzy neural network (eT2RFNN) is proposed in this paper. eT2RFNN features a novel recurrent network architecture, possessing double local recurrent connections. It generates a generalized interval type-2 fuzzy rule, where an interval type-2 multivariate Gaussian function constructs the rule premise, and the rule consequent is crafted by the nonlinear wavelet function. eT2RFNN adopts an open structure, where it can start learning process from scratch with an empty rule base. Fuzzy rules can be automatically generated according to degree of nonlinearity data stream conveys. It can performs a rule base simplification procedure by pruning and merging inactive, outdated and overlapping rules. eT2RFNN can deal with the high dimensionality problem, where an online dimensionality reduction method is integrated in the training process. The efficacy of the eT2RFNN has been numerically validated using two real-world data streams, where it provides high predictive accuracy, while retaining low complexity.

关键词： Evolving Fuzzy Systems Fuzzy Neural Networks Type-2 Fuzzy Systems Sequential Learning Fuzzy neural networks Fuzzy rules Serial learning Sequential Learning Open Architecture Gauss equation data flow ENVIRONMENTAL IMPACT STATEMENTS Conveying rule base tree surgery

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Spatiotemporal activation time estimation improves noninvasive localization of cardiac electrical activity

Spatiotemporal activation time estimation improves noninvasi...

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computers in Cardiology (CinC)

作者： Matthijs J.M. Cluitmans Jaume Coll-Font Burak Erem Dana Brooks Pietro Bonizzi Joël M.H. Karel Paul G.A. Volders Ralf L.M. Peeters Ronald L. Westra Department of Data Science and Knowledge Engineering Maastricht University Maastricht The Netherlands Department of Electrical & Computer Engineering Northeastern University Boston MA USA Boston Childrens Hospital Harvard Medical School Boston MA USA Department of Data Science and Knowledge Engineering Maastricht University The Netherlands CARIM School for Cardiovascular Diseases Maastricht University The Netherlands

ISBN: (纸本)9781509008964

Electrocardiographic imaging (ECGI) reconstructs epicardial potentials and electrograms from body-surface electrocardiograms and a torso-heart geometry. For clinical purposes, local activation and recovery times are often more useful than epicardial electrograms. However, noise and fractionation can affect estimation of activation and recovery times from reconstructed electrograms. Here, we employ a method for activation and recovery time estimation that detects the simultaneous presence of spatial and temporal features associated with a passing wavefront and evaluate this in a series of canine experiments. We show that estimation of activation times is more accurate when this spatiotemporal approach is used, however, recovery times are best determined with a temporal-only approach. Additional spatial smoothing further benefits activation and recovery time estimation in all cases. This results in a median beat origin localization error of only one centimeter, which could expedite catheter-based diagnostic evaluation and ablation in clinical settings.

关键词： Estimation Smoothing methods Spatiotemporal phenomena Electric potential Heart Imaging Electrodes

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Discovery of research interests of authors over time using a topic model

Discovery of research interests of authors over time using a...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Young-Seob Jeong Sang-Hun Lee Gahgene Gweon Department of Computer Science KAIST Yuseong-gu Daejeon South Korea Agency for Defense Development Daejeon South Korea Department of Knowledge Service Engineering KAIST Daejeon South Korea

With a growing number of Web documents, many approaches have been proposed for knowledge discovery on Web documents. The documents do not always provide keywords or categories, so unsupervised approaches are desirable, and topic modeling is such an approach for knowledge discovery without using labels. Further, Web documents usually have time information such as publish years, so knowledge patterns over time can be captured by incorporating the time information. In this paper, we propose a new topic model called the Author Topic-Flow (ATF) model whose objective is to capture temporal patterns of research interests of authors over time, where each topic is associated with a research domain. The design of the ATF model is based on the hypothesis that direct topic flows are better than indirect topic flows in the state-of-the-art Temporal Author Topic (TAT) model, which is the most similar approach to ours. We prove the hypothesis by showing the effectiveness of the ATF model compared to the TAT model.

关键词： Semantics Electronic mail knowledge discovery Resource management Analytical models Market research

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Bi-directional search for robust routes in time-dependent bi-criteria road networks 15

Bi-directional search for robust routes in time-dependent bi...

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15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2015

作者： Mihalák, Matú Montanari, Sandro Department of Knowledge Engineering Maastricht University Netherlands Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9783939897996

Based on time-dependent travel times for N past days, we consider the computation of robust routes according to the min-max relative regret criterion. For this method we seek a path minimizing its maximum weight in any one of the N days, normalized by the weight of an optimum for the respective day. In order to speed-up this computationally demanding approach, we observe that its output belongs to the Pareto front of the network with time-dependent multi-criteria edge weights. We adapt a well-known algorithm for computing Pareto fronts in time-dependent graphs and apply the bi-directional search technique to it. We also show how to parametrize this algorithm by a value K to compute a K-approximate Pareto front. An experimental evaluation for the cases N = 2 and N = 3 indicates a considerable speed-up of the bi-directional search over the uni-directional.

关键词： Travel time

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