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Contextual distance for data perception

Contextual distance for data perception

引用

11th IEEE international conference on Computer Vision

作者： Zhao, Deli Lin, Zhouchen Tang, Xiaoou Microsoft Res Asia Visual Comp Grp Beijing Peoples R China

ISBN: (纸本)9781424416301

Structural perception of data plays a fundamental role in pattern analysis and machine learning. In this paper, we develop a new structural perception of data based on local contexts. We first identify the contextual set of a point by finding its nearest neighbors. then the contextual distance between the point and one of its neighbors is defined by the difference between their contribution to the integrity of the geometric structure of the contextual set, which is depicted by a structural descriptor the centroid and the coding length are introduced as the examples of descriptors of the contextual set. Furthermore, a directed graph (digraph) is built to model the asymmetry of perception. the edges of the digraph are weighted based on the contextual distances. thus direction is brought to the undirected data. And the structural perception of data can be performed by mining the properties of the digraph. We also present the method for deriving the global digraph Laplacian from the alignment of the local digraph Laplacians. Experimental results on clustering and ranking of toy problems and real data show the superiority of asymmetric perception.

关键词： Directed graphs

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Content recommendation system based on private dynamic user profile

Content recommendation system based on private dynamic user ...

引用

6th international conference on machine learning and Cybernetics

作者： Chen, Ting Han, Wei-Li Wang, Hai-Dong Zhou, Yi-Xun Xu, Bin Zang, Bin-Yu Fudan Univ Software Sch Shanghai Peoples R China

ISBN: (纸本)9781424409723

As the amount of the accessible information in the Internet is overwhelming, personalized content recommendation system offers spam filtering service and suggests useful information to the end users. It is a hotspot in the research area of content management on WWW. Traditional recommendation systems do the data mining on web access logs, discover user's access patterns, and filter the information on behalf of the user at the server side. One critical limitation of traditional recommendation system is the lack of user's private daily data, such as schedules, favorite websites and personal emails. the reason for this limitation is the privacy leak issue when the server holds much more private user data. To solve this problem, this paper presents an agent-based personalized recommendation method called Content REcommendation System based on private Dynamic User Profile (CRESDUP). the system collects and mines the private data of user at the client side, discovers, stores and updates private Dynamic User Profile (DUP) at the client side. the system fetches preferred message from the content server according to DUP. An important usage of this technology is a personalized advertising system in the RSS (Rich Site Summary, or RDF Site Summary) reader application. Our experiment shows that the system can utilize DUP to identify the customers' potential preferences and deliver the more preferred messages, especially the advertisements, to people who are interested.

关键词： content recommendation dynamic user profile privacy-enhanced personalization data mining RSS

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CAMEL: An intelligent computational model for agro-meteorological data

CAMEL: An intelligent computational model for agro-meteorolo...

引用

6th international conference on machine learning and Cybernetics

作者： Leung, Carson K. -S. Mateo, Mark A. F. Nadler, Andrew J. Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada Manitoba Agr Food & Rural Initiat MAFRI Winnipeg MB Canada

ISBN: (纸本)9781424409723

Weather plays an important role in agriculture. this calls for reliable weather information, which in turn helps farmers make management decisions about their crops. In this paper, we propose an intelligent Computational model for Agro-MEteoroLogical data (CAMEL). the model serves three purposes. First, it effectively captures important information about large amounts of data collected from various weather stations distributed in a wide geographic expanse. Second, the proposed model learns from historical data and predicts future trends. this helps us obtain accurate weather forecasts. third, through the prediction of weather trends, CAMEL gives us a better understanding of agro-meteorological data. When we compare the predicted results with the observed data, any significant difference between them may be an indication of equipment malfunction or other problems. In this way, CAMEL helps us detect abnormal data and facilitates in guarding against potential sources of error. Consequently, well-functioning equipment and accurate weather data help farmers make wise crop management decisions. Experimental results on real-life datasets show the effectiveness of our proposed intelligent computational model for agro-meteorological data.

关键词： intelligent computational model knowledge discovery and data mining machine learning neural network prediction outlier detection data quality control quality assurance weather data

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Variable width elliptic Gaussian kernels for probability density estimation

Variable width elliptic Gaussian kernels for probability den...

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8th international conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services

作者： Pokrajac, Dragoljub Latecki, Longin Jan Lazarevic, Aleksandar Nikolic, Jelena Delaware State Univ Dept Comp & Informat Sci Appl Math Res Ctr 1200 N DuPont Hwy Dover DE USA Temple Univ CIS Dept Philadelphia PA 19122 USA CREOSA & United Technol Res Ctr E Hartford CT 06108 USA Univ Nis Fac Elect Engn Nish Serbia Delaware State Univ Dept Comp & Informat Sci Ctr Res & Educ Opt Sci & Appl Dover DE USA

ISBN: (纸本)9788685195549

Estimation of probability density functions based on available data is important problem arising in various fields, such as telecommunications, machine learning, data mining, pattern recognition and computer vision. In this paper, we consider Kernel-based non-parametric density estimation methods and derive formulae for variable kernel density estimation using generalized, elliptic Gaussian kernels. the proposed technique is verified on simulated data.

关键词： pattern recognition non-parametric density estimation variable kernel density estimation generalized Gaussian kernels

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Understanding challenges in preserving and reconstructing computer-assisted medical decision processes

Understanding challenges in preserving and reconstructing co...

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6th international conference on machine learning and Applications

作者： Lee, Sang-Chul Bajcsy, Peter Univ Illinois Natl Ctr Supercomp Applicat Urbana IL 61801 USA

ISBN: (纸本)9780769530697

this paper addresses the problem of understanding preservation and reconstruction requirements for computer-aided medical decision-making. With an increasing number of computer-aided decisions having a large impact on our society, the motivation for our work is not only to document these decision processes semi-automatically but also to understand the preservation cost and related computational requirements. Our objective is to support computer-assisted creation of medical records, to guarantee authenticity of records, as well as to allow managers of electronic medical records (EMR), archivists and other users to explore and evaluate computational costs (e.g., storage and processing time) depending on several key characteristics of appraised records. Our approach to this problem is based on designing an exploratory simulation framework for investigating preservation tradeoffs and assisting in appraisals of electronic records. We have a prototype simulation framework called Image Provenance To Learn (IEP2Learn) to support computer-aided medical decisions based on visual image inspection. the current software enables to explore some of the tradeoffs related to (1) information granularity (category and level of detail), (2) representation of provenance information, (3) compression, (4) encryption, (5) watermarking and steganography, (6) information gathering mechanism, and (7) final medical report content (level of detail) and its format. We illustrate the novelty of IP2Learn by performing example studies and the results of tradeoff analyses for a specific image inspection task.

关键词： biomedical decision support systems medical informatics electronic medical record (EMR) data mining

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machine learning for question answering from tabular data

Machine learning for question answering from tabular data

引用

18th international conference on database and Expert Systems Applications

作者： Khalid, Mahboob Alam Jijkoun, Valentin de Rijke, Maarten Univ Amsterdam ISLA Kruislaan 403 NL-1098 SJ Amsterdam Netherlands

ISBN: (纸本)9780769529325

Question Answering (QA) systems automatically answer natural language questions in a human-like manner One of the practical approaches to open domain QA consists in extracting facts from free text offline and using a lookup mechanism when answering user's questions online. this approach is related to natural language interfaces to databases (NLIDBs) that were studied extensively from the 1970s to the 1990s. NLIDB systems employed a range of techniques, from simple pattern-matching rules to formal logical calculi such as the lambda calculus, but most were restricted to specific domains. In this paper we describe a machine learning approach to querying tabular data for QA which is not restricted to specific domains. Our approach consists of two steps: for an incoming question, we first use a classifier to identify appropriate tables and columns in a structured database, and then employ a free-text retrieval to look up answers. the system uses part-of-speech tagging, named-entity normalization and a statistical classifier trained on data from the TREC QA task. With the TREC QA data, our system is shown to significantly outperform an existing rule-based table lookup method.

关键词： Table lookup

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Embedded Support Vector machine : Architectural Enhancements and Evaluation

Embedded Support Vector Machine : Architectural Enhancements...

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international conference on VLSI Design

作者： Soumyajit Dey Monu Kedia Niket Agarwal Anupam Basu Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9780769527628;0769527620

In recent years, research and development in the field of machine learning and classification techniques have gained paramount importance. the future generation of intelligent embedded devices will obviously require such classifiers working on-line and performing classification tasks in a variety of fields ranging from data mining to recognition tasks in image and video. Among different such techniques, support vector machines (SVMs) have been found to deliver state of the art performance thus emerging as the clear winner. In this work, the support vector machine learning and classification tasks are evaluated on embedded processor architectures and subsequent architectural modifications are proposed for performance improvement of the same

关键词： Support vector machines Support vector machine classification machine learning learning systems Coprocessors Application software data mining Image recognition Natural languages Computer science

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A tongue-print image database for recognition

A tongue-print image database for recognition

引用

6th international conference on machine learning and Cybernetics

作者： Liu, Zhi Yan, Jing-Qi Zhang, David Tang, Qun-Lin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Hong Kong Polytech Univ Biometr Res Ctr Dept Comp Kowloon Peoples R China Grad Sch Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781424409723

the tongue is a unique organ in that it can be stuck out of mouth for inspection, in this act offering a proof of life, and yet it is otherwise well protected in the mouth and is difficult to forge. the tongue also presents both geometric shape information and physiological texture information which are potentially useful in identity verification applications. Furthermore, the act of physically reaching or thrusting out is a convincing proof for the liveness. Despite these obvious advantages, little work has hitherto been done on tongue features. A primary factor for preventing such research is the lack of an available 3D tongue image database. In this paper, we present a newly developed 3D tongue image database, which includes. both tongue geometric shape and surface textures of 134 subjects. this is the first attempt at making a 3D tongue image database available for the research community, with the ultimate goal of fostering;the research on tongue biometrics. the new database can be a valuable resource for algorithm assessment, comparison and evaluation.

关键词： tongue-prints database biometrics verification

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Distributed and shared memory algorithm for parallel mining of association rules 1

引用

5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

作者： Palancar, J. Hernández Tormo, O. Fraxedas Cárdenas, J. Festán Leán, R. Hernández 7a # 21812 e/ 218 y 222 Rpto. Siboney Playa La Habana Cuba

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

the search for frequent patterns in transactional databases is considered one of the most important data mining problems. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. Almost all of these algorithms make repeated passes over the dataset to determine the set of frequent itemsets, thus implying high I/O overhead. In the parallel case, most algorithms perform a sum-reduction at the end of each pass to construct the global counts, also implying high synchronization cost. We present a novel algorithm that exploits efficiently the trade-offs between computation, communication, memory usage and synchronization. the algorithm was implemented over a cluster of SMP nodes combining distributed and shared memory paradigms. this paper presents the results of our algorithm on different data sizes experimented on different numbers of processors, and studies the effect of these variations on the overall performance. © Springer-Verlag Berlin Heidelberg 2007.

关键词： data mining

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Hybrid unsupervised/Supervised virtual reality spaces for visualizing cancer databases:: An evolutionary computation approach

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9th international Work-conference on Artificial Neural Networks

作者： Valdes, Julio J. Barton, Alan J. Natl Res Council Canada M501200 Montreal Rd Ottawa ON K1A 0R6 Canada

ISBN: (纸本)9783540730064

this paper introduces a multi-objective optimization approach to the problem of computing virtual reality spaces for the visual representation of relational structures (e.g. databases), symbolic knowledge and others, in the context of visual data mining and knowledge discovery. Procedures based on evolutionary computation are discussed. In particular, the NSGA-II algorithm is used as a framework for an instance of this methodology;simultaneously minimizing Sammon's error for dissimilarity measures, and mean cross-validation error on a k-nn pattern classifier. the proposed approach is illustrated with an example from cancer genomics data (e.g. lung cancer) by constructing virtual reality spaces resulting from multi-objective optimization. Selected solutions along the Pareto front approximation are used as nonlinearly transformed features for new spaces that compromise similarity structure preservation (from an unsupervised perspective) and class separability (from a supervised pattern recognition perspective), simultaneously. the possibility of spanning a range of solutions between these two important goals, is a benefit for the knowledge discovery and data understanding process. the quality of the set of discovered solutions is superior to the ones obtained separately, from the point of view of visual data mining.

关键词： Unsupervised learning

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