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1st International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2012

作者： Manjunath, M. Srinivasa, K.G. Muppalla, Anil Kumar Machine Learning and Data Mining Laboratory Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India

ISBN: (纸本)9788132211563

Intrusion detection system (IDS) consists of set of techniques and methods for collection of packets from host system or network and analyzes those packets for anomalous content. IDSs mainly fall into two categories: signature-based IDSs and anomaly detection systems. A rule-based IDS compares the incoming packets against rule set in order to detect intrusion. A common approach followed is to build rule trees or finite automata with rule set and traverse it using a packet as input string. 30-60 % of total signature-based IDS processing time is spent on pattern matching [1]. The existing signature-based IDS cannot meet the speed demands imposed by both high network speeds and increasing number of signatures, and more CPU time is spent on searching for rules that match each packet. In this paper, we are going to present an analysis on IDS that is combined with other methods and techniques to produce greater results and hence contribute to the improvement of IDS. © 2014 Springer India.

关键词： Pattern matching

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Efficient image representation for object recognition via pivots selection

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Frontiers of Computer Science 2014年

作者： Xie, Bojun Liu, Yi Zhang, Hui Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well-known pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to "grow-up" features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive *** this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD. © 2014 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： Object recognition

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Big data: Prospects and Challenges

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Vikalpa 2015年第1期40卷 74 - 96页

作者： Janakiraman Moorthy Rangin Lahiri Neelanjan Biswas Dipyaman Sanyal Jayanthi Ranjan Krishnadas Nanath Pulak Ghosh (Coordinator) Director and Professor of Marketing at the Institute of Management Technology Dubai. Earlier he was Professor of Marketing at the IIM Calcutta and IIM Lucknow. He received his PhD from IIM Ahmedabad. His recent research papers were published in the leading scholarly ournals such as Marketing Science British Food Journal Journal of Information Technology Case and Application Research Journal of Database Marketing & Customer Strategy Management. He has wide experience in the banking and investment industry. He was earlier the Global Research and Project Director of the Institute for Customer Relationship Management Atlanta USA. He was the Convener of the prestigious CAT Exam 2011. e-mail: Practice Director leading Atos India's CRM practice while supporting Strategic Business Development for North American Market. With an experience of more than 15 years Rangin has worked extensively as a Business Consultant in Information Technology (Sales Automation Marketing & Service Management area) Customer Data Management and CRM Analytics. e-mail: Business Consultant at Atos with extensive experience in Business Analysis Risk Management Analytics Business Development Presales Solution Ideation on Enterprise Data Management Enterprise Reference Data and Master Data Management area. e-mail: founder and CEO of dono consulting a boutique quantitative analytics and investment research firm. He has worked for leading financial firms in New York and India including Dow Jones Blackstone Sorin Capital (VP Quantitative Modeling) and Thomson Reuters (Head of Real Estate Analytics). A CFA charter holder and Commonwealth Scholar Deep has an MS (Applied Economics) from University of Texas Dallas and an MA (Economics) from Jadavpur University e-mail: Professor in the Information Systems Group of the Institute of Management Technology Ghaziabad. Her PhD is in the field of data mining from Jamia Millia Islamia Central University India. She has published five edited books. She is serving on the editorial b

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A combinatorial algebraic approach for the identifiability of low-rank matrix completion

A combinatorial algebraic approach for the identifiability o...

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29th International Conference on machine learning, ICML 2012

作者： Király, Franz Tomioka, Ryota Machine Learning Group Franklinstr. 28/29 10587 Berlin Germany FU Berlin Discrete Geometry Group Arnimallee 2 14195 Berlin Germany University of Tokyo Information-Theoretic Machine Learning and Data Mining Group 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

ISBN: (纸本)9781450312851

In this paper, we review the problem of matrix completion and expose its intimate relations with algebraic geometry, combinatorics and graph theory. We present the first necessary and sufficient combinatorial conditions for matrices of arbitrary rank to be identifiable from a set of matrix entries, yielding theoretical constraints and new algorithms for the problem of matrix completion. We conclude by algorithmically evaluating the tightness of the given conditions and algorithms for practically relevant matrix sizes, showing that the algebraic-combinatorial approach can lead to improvements over state-of-the-art matrix completion methods. Copyright 2012 by the author (s)/owner (s).

关键词： Matrix algebra

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Aid to regional development agencies: Finding and matching research funding opportunities

Aid to regional development agencies: Finding and matching r...

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13th Annual International Digital Government Research Conference: Bridging Research and Practice, dg.o 2012

作者： Kaschesky, Michael Gschwend, Adrian Bouchard, Guillaume Furrer, Patrick Gamard, Stephane Riedl, Reinhard Bern University of Applied Sciences E-Government Unit Bern Switzerland Xerox Research Center Europe Data Mining and Machine Learning Grenoble France EuresearCH Effingerstrasse 19 Bern Switzerland SalsaDev SA Chemin de Aulx 18 Plan-les-Ouates Switzerland

ISBN: (纸本)9781450314039

Regional development agencies are confronted with a plethora of research funding programs that provide opportunities for regional research groups and SMEs. This paper describes the practical benefits and technological building blocks of an online matching service of research profiles with funding opportunities. It gives an overview of the infrastructure for data sourcing and processing based on a scalable cloud computing platform. It then describes how data is consolidated, analyzed, and interlinked so as to facilitate the finding and matching of funding opportunities. Integrating concepts and themes with those available as Linked Open data (LOD) adds value to finding and matching by interlinking results with publicly available data resources, and to improve search performance for related content on the internet. In order to optimize matching, the paper describes the user-profiling capabilities based on statistical analyses and machine learning. The paper concludes by discussing the lessons learned so far as well as possible extensions into other application areas. © 2012 ACM.

关键词： learning systems

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Opinion mining in social media: Modeling, simulating, and visualizing political opinion formation in the web 11

Opinion mining in social media: Modeling, simulating, and vi...

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12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, dg.o 2011

作者： Kaschesky, Michael Sobkowicz, Pawel Bouchard, Guillaume Bern University of Applied Sciences E-Government Unit Bern Switzerland Xerox Research Center Europe Data Mining and Machine Learning Grenoble France

ISBN: (纸本)9781450307628

Affordable and ubiquitous online communications (social media) provide the means for flows of ideas and opinions and play an increasing role for the transformation and cohesion of society - yet little is understood about how online opinions emerge, diffuse, and gain momentum. To address this problem, an opinion formation framework based on content analysis of social media and sociophysical system modeling is proposed. Based on prior research and own projects, three building blocks of online opinion tracking and simulation are described: (1) automated topic and opinion detection in real-time, (2) topic and opinion modeling and agent-based simulation, and (3) visualizations of topic and opinion networks. Finally, two application scenarios are presented to illustrate the framework and motivate further research. © 2011 ACM.

关键词： Standardization

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Toward personalized care management of patients at risk- The diabetes case study 11

Toward personalized care management of patients at risk- The...

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17th ACM SIGKDD International Conference on Knowledge Discovery and data mining, KDD 2011

作者： Neuvirth, Hani Ozery-Flato, Michal Laserson, Jonathan Rosen-Zvi, Michal Hu, Jianying Kohn, Martin S. Ebadollahi, Shahram Machine Learning and Data Mining Group IBM Research Mount Carmel Haifa 31905 Israel Healthcare Transformation Group IBM Research Hawthorne NY United States

ISBN: (纸本)9781450308137

Chronic diseases constitute the leading cause of mortality in the western world, have a major impact on the patients'quality of life, and comprise the bulk of healthcare costs. Nowadays, healthcare data management systems integrate large amounts of medical information on patients, including diagnoses, medical procedures, lab test results, and more. Sophisticated analysis methods are needed for utilizing these data to assist in patient management and to enhance treatment quality at reduced costs. In this study, we take a first step towards better disease management of diabetic patients by applying state-of-the art methods to anticipate the patient's future health condition and to identify patients at high risk. Two relevant outcome measures are explored: the need for emergency care services and the probability of the treatment producing a sub-optimal result, as defined by domain experts. By identifying the high-risk patients our prediction system can be used by healthcare providers to prepare both financially and logistically for the patient needs. To demonstrate a potential downstream application for the identified high-risk patients, we explore the association between the physician treating these patients and the treatment outcome, and propose a system that can assist healthcare providers in optimizing the match between a patient and a physician. Our work formulates the problem and examines the performance of several learning models on data from several thousands of patients. We further describe a pilot system built on the results of this analysis. We show that the risk for the two considered outcomes can be evaluated from patients'characteristics and that features of the patient-physician match improve the prediction accuracy for the treatment's success. These results suggest that personalized medicine can be valuable for high risk patients and raise interesting questions for future improvements. Copyright 2011 ACM.

关键词： Risk assessment

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Cloud-based event-processing architecture for opinion mining

Cloud-based event-processing architecture for opinion mining

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2011 IEEE World Congress on Services, SERVICES 2011

作者： Grivas, Stella Gatziu Schaaf, Marc Kaschesky, Michael Bouchard, Guillaume Information and Knowledge Management Unit University of Applied Sciences Switzerland E-Government Unit Bern University of Applied Sciences Switzerland Data Mining and Machine Learning Group Xerox Research Center Europe Grenoble France

ISBN: (纸本)9780769544618

The viability of cloud computing for information-intensive tasks arising in real-time opinion mining and sentiment analysis of large online text streams is described. We show how a smart distributed architecture enables an efficient and scalable design for opinion mining on internet-based content that answers key challenges, such as integrating heterogeneous data sources and adapting to events through dynamic system configuration. In particular, we present a novel approach of semantic complex event processing in a cloud environment capturing different levels of information, such as event data (e.g. content from various heterogeneous, distributed sources) as well as associations identified during the opinion mining and sentiment analysis process (e.g. dynamic co-reference resolution). © 2011 IEEE.

关键词： Sentiment analysis

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Cloud-Based Event-Processing Architecture for Opinion mining

Cloud-Based Event-Processing Architecture for Opinion Mining

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IEEE World Congress on Services (SERVICES)

作者： Stella Gatziu Grivas Marc Schaaf Michael Kaschesky Guillaume Bouchard Information and Knowledge Management Unit University of Applied Science Switzerland E-Government Unit Bern University of Applied Sciences Switzerland Data Mining and Machine Learning Group Xerox Research Centre Europe Grenoble France

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Application of SVM in Citation Information Extraction

Application of SVM in Citation Information Extraction

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International Workshop on Complexity and data mining (IWCDM)

作者： Jiguang Liang Robert Layton Wei Wang Web Data Mining Laboratory Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China Internet Commerce Security Laboratory University of Ballarat Ballarat Australia Machine Learning and Cognition Laboratory Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China

Support Vector machines are an effective form of binary-class classification algorithm. To enhance the utilization of text structural features for information extraction, which are greatly restricted by the Hidden Markov Model (HMM), this paper proposes a support vector machine multi-class classification based on Markov properties to extract the information from a citation database. The proposed model extracts symbol characteristics as features and composes a binary tree of the transition probabilities. Experiments show that the proposed method outperforms HMM and basic SVM methods.

关键词： Hidden Markov models Support vector machines Feature extraction Binary trees data mining Markov processes Probability

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