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检索条件"主题词=Algorithm Design And Analysis"

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Sparse coding with sparse dictionaries for credit risk classification

Sparse coding with sparse dictionaries for credit risk class...

引用

IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Xueyan Mei School of Professional Studies Columbia University New York City NY USA

ISBN: (纸本)9781509034857

Credit risk analysis seeks to determine whether a customer is likely to default on the financial obligation, which is a very important problem in finance. In this paper, we will present a machine learning framework to deal with this problem by formulating it as a binary classification problem. The framework consists of two parts: dictionary learning and classifier training. Firstly, we introduce a sparse K-SVD method to discovery a sparse dictionary to represent the training data, which will contribute to good performance and efficient computation in subsequent classification. Secondly, SVM classifiers are training on the new representations of the training data. The trained classifiers will serve as a credit risk analysis expert to automatically classify the testing data. We evaluate the framework in a real-world credit risk prediction task, and the empirical results demonstrate the advantage of the method by comparing with other strategies.

关键词： Dictionaries Encoding Support vector machines Risk analysis Classification algorithms algorithm design and analysis Training

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Study on the influences of fractional frequency offset on PLC synchronization in DOCSIS 3.1

Study on the influences of fractional frequency offset on PL...

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： JaeHwui Bae JinHyuk Song Sang-Jung Ra Dong-Joon Choi Joon-Young Jung Namho Hur Broadcasting System Research Department Electronics and Telecommunications Research Institute Daejeon Republic of Korea

ISBN: (纸本)9781509013265

In this paper, we present the simulation results of FFO(fractional frequency offset) influences on PLC preamble correlation based PLC synchronization in DOCSIS 3.1 Downstream. FFO makes inter-carrier interferences and breaks orthogonality in OFDM based transmission system. It degrades the performance of OFDM by reducing SNR(signal to noise ratio). The performance of PLC synchronization is influenced by the reduced SNR caused by FFO. We analyzed the influences of FFO on PLC synchronization by applying various FFO values. We showed the PLC synchronization performances to various FFO values and discussed the results considering requirements of FFO compensation performance in OFDM signal reception.

关键词： Correlation OFDM Synchronization algorithm design and analysis Estimation Frequency synchronization Time-frequency analysis

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Extracting Log Patterns from System Logs in LARGE

Extracting Log Patterns from System Logs in LARGE

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yining Zhao Haili Xiao Chinese Academy of Sciences Computer Network Information Center Beijing China

ISBN: (纸本)9781509036837

This paper introduces algorithms of extracting log patterns from system logs in LARGE system. LARGE is a log analyzing framework deployed in the Scientific Computing Grid Environment in Chinese Academy of Sciences. In the process of monitoring systems logs, there is the need of obtaining patterns from log records, and set corresponding rules to deal with logs in certain patterns. Two algorithms named identical words rate and tree-matching to fulfill this requirement are illustrated, and their performances are compared to show the capability and efficiency of extracting log patterns. This work can be extended to various distributed and cloud environments for log monitoring.

关键词： Data mining Monitoring Clustering algorithms algorithm design and analysis Middleware Scientific computing Optimization

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Online Contextual Influence Maximization in social networks

Online Contextual Influence Maximization in social networks

引用

Annual Allerton Conference on Communication, Control, and Computing

作者： Ömer Sarıtaç Altuğ Karakurt Cem Tekin Department of Industrial Engineering Bilkent University Department of Electrical and Electronics Engineering Bilkent University

In this paper, we propose the Online Contextual Influence Maximization Problem (OCIMP). In OCIMP, the learner faces a series of epochs in each of which a different influence campaign is run to promote a certain product in a given social network. In each epoch, the learner first distributes a limited number of free-samples of the product among a set of seed nodes in the social network. Then, the influence spread process takes place over the network, other users get influenced and purchase the product. The goal of the learner is to maximize the expected total number of influenced users over all epochs. We depart from the prior work in two aspects: (i) the learner does not know how the influence spreads over the network, i.e., it is unaware of the influence probabilities; (ii) influence probabilities depend on the context. We develop a learning algorithm for OCIMP, called Contextual Online INfluence maximization (COIN). COIN can use any approximation algorithm that solves the offline influence maximization problem as a subroutine to obtain the set of seed nodes in each epoch. When the influence probabilities are Hölder continuous functions of the context, we prove that COIN achieves sublinear regret with respect to an approximation oracle that knows the influence probabilities for all contexts. Moreover, our regret bound holds for any sequence of contexts. We also test the performance of COIN on several social networks, and show that it performs better than other methods.

关键词： Context Social network services Approximation algorithms Integrated circuit modeling Context modeling algorithm design and analysis

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Robust image segmentation using global and local fuzzy energy based active contour

Robust image segmentation using global and local fuzzy energ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Ajoy Mondal K. Ramachandra Murthy Ashish Ghosh Susmita Ghosh Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE Jadavpur University Kolkata India

ISBN: (纸本)9781509006274

Though various image segmentation techniques have been developed, it is still a very challenging task to design a robust and efficient algorithm to segment (noisy, blurred or even discontinuous edged) images having high intensity inhomogeneity or non-homogeneity. In this article, a robust fuzzy energy based active contour, using both global and local information, is proposed to detect objects in a given image based on curve evolution. The local energy is generated by considering both local spatial and gray level/color information. The proposed model can better deal with images having high intensity inhomogeneity or non-homogeneity, noise and blurred boundary or discontinuous edges by incorporating local energy term in the proposed active contour energy function. The global energy term is used to avoid unsatisfactory results due to bad initialization. We show a realization of the proposed method and demonstrate its performance (both qualitatively and quantitatively) with respect to state-of-the-art techniques on several images having such kind of artifacts. analysis of results concludes that the proposed method can detect objects from given images in a better way than the existing ones.

关键词： Image segmentation Active contours Nonhomogeneous media Robustness Image edge detection Computational modeling algorithm design and analysis

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Multi-dimensional and customizable open-source labware for promoting big data analytical skills in STEM education

Multi-dimensional and customizable open-source labware for p...

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Frontiers in Education (FIE) Conference

作者： Ying Xie Kai Qian Jing He Department of Computer Science Kennesaw State University Kennesaw Georgia USA

ISBN: (纸本)9781509017911

In order to remove resource barriers and smooth the learning curve for education on big data analytics in STEM disciplines, we develop an portable open source labware that is called STEM-BD for promoting education on big data analytics. STEM-BD integrates the following four critical components, big data platform, big data sets, data analytics algorithms and hands-on lab exercises in a multi-dimensional and customizable way. In this paper, we provide a detailed description of the design goal of STEM-BD, its prototype, preliminary evaluation results, and future development.

关键词： Big data Classification algorithms algorithm design and analysis Clustering algorithms Sparks Machine learning algorithms Education

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Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluation

Apache Mahout's k-Means vs Fuzzy k-Means Performance Evaluat...

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International Conference on Intelligent Networking and Collaborative Systems (INCOS)

作者： Fatos Xhafa Adriana Bogza Santi Caballé Leonard Barolli Universitat Politècnica de Catalunya Barcelona Spain SmartLearn Group Universitat Oberta de Catalunya Spain Universitat Oberta de Catalunya Barcelona Spain Fukuoka Institute of Technology Fukuoka Japan

The emergence of the Big Data as a disruptive technology for next generation of intelligent systems, has brought many issues of how to extract and make use of the knowledge obtained from the data within short times, limited budget and under high rates of data generation. The foremost challenge identified here is the data processing, and especially, mining and analysis for knowledge extraction. As the 'old' data mining frameworks were designed without Big Data requirements, a new generation of such frameworks is being developed fully implemented in Cloud platforms. One such frameworks is Apache Mahout aimed to leverage fast processing and analysis of Big Data. The performance of such new data mining frameworks is yet to be evaluated and potential limitations are to be revealed. In this paper we analyse the performance of Apache Mahout using large real data sets from the Twitter stream. We exemplify the analysis for the case of two clustering algorithms, namely, k-Means and Fuzzy k-Means, using a Hadoop cluster infrastructure for the experimental study.

关键词： Clustering algorithms Data mining Big data algorithm design and analysis Frequency measurement Twitter Electronic mail

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A comprehensive survey for sentiment analysis tasks using machine learning techniques

A comprehensive survey for sentiment analysis tasks using ma...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Ebru Aydoğan M. Ali Akcayol Department of Computer Engineering Gazi University Ankara Turkey

Sentiment analysis is one of the most popular research topics in last years. There are lots of data on web which require analysis in order for them to become useful. Many researchers have focused on making sense of these data. Therefore, sentiment analysis concept is proposed. Sentiment analysis methods try to emerge any opinions, feelings, and subjectivity behind the text. Machine learning algorithms and vocabulary based methods are used to perform sentiment analysis. In this research, (i) recently studied researches on machine learning based sentiment analysis are investigated to give background; (ii) they are classified according to their tasks on extracting information; (iii) the encountered and potential challenges on this research topic are revisited and discussed.

关键词： Sentiment analysis Support vector machines Niobium algorithm design and analysis Machine learning algorithms Unsupervised learning Classification algorithms

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Migrating GIS Big Data Computing from Hadoop to Spark: An Exemplary Study Using Twitter

Migrating GIS Big Data Computing from Hadoop to Spark: An Ex...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Zhibo Sun Hong Zhang Zixia Liu Chen Xu Liqiang Wang Department of Computer Science University of Central Florida USA Department of Geography University of Wyoming USA

Recent research has demonstrated that social media could provide valuable spatio-temporal data about users activities. However, information extraction and computation from big amount of data pose various challenges. To effectively process massive datasets, several platforms have been developed. Our previous study [20] explored Hadoop-based cloud computing for processing big amount of social media data [9] to study geographic distributions of social media users. In this paper, we investigate an emerging system named Spark and present a timely pilot experience on geospatial big data research. In our study, Spark has been utilized to perform some classic geospatial analyses like K-Nearest Neighbors (KNN), geographic mean and median points, and the distribution of the median points. Our design is tested on an Amazon EC2 cluster. An exemplary study using 60GB, 120GB and 180GB Twitter data has demonstrated the performance achievements by migrating computing tasks from Hadoop to Spark. In our experiments, the Spark-based solution can be up to 2.3x faster than the Hadoop-based solution due to its in-memory processing and coarse-grained resource allocation strategy. In the paper, we also discuss optimization strategies on using Spark for different geospatial computing tasks.

关键词： Sparks Geospatial analysis Big data Twitter algorithm design and analysis

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Achieving fault-tolerance and safety of discrete-event systems through learning

Achieving fault-tolerance and safety of discrete-event syste...

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American Control Conference

作者： Jin Dai Ali Karimoddini Hai Lin Department of Electrical Engineering University of Notre Dame IN 46556 USA Department of Electrical and Computer Engineering North Carolina A&T University Greensboro 27411 USA

ISBN: (纸本)9781467386838

A system is said to be fault-tolerant if it remains functional even after a fault occurs. By describing faults as unpredicted events, we study the active fault-tolerance of discrete-event systems (DES) while ensuring safety requirements. Starting from a finite automaton model of the uncontrolled plant, our proposed control framework consists of nominal supervision, fault diagnosis and active post-fault control reconfiguration. First a nominal supervisor is designed with respect to the nominal mode to ensure the control specification prior to the occurrence of faults. Second, a learning-based algorithm is proposed to compute a diagnoser that can detect the occurrence of a fault. Necessary and sufficient conditions under which a post-fault safety-enforcing control reconfiguration is feasible are explored, and a second learning-based design algorithm for the post-fault supervisor is presented by using the limited lookahead policies. Effectiveness the proposed framework is examined through an example.

关键词： Fault tolerance Fault tolerant systems Fault diagnosis Safety algorithm design and analysis Learning automata Automata

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