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Convex hulls as an hypothesis language bias

Convex hulls as an hypothesis language bias

引用

4th international conference on data mining

作者： Newlands, DA Webb, GI Deakin Univ Geelong Vic 3217 Australia

ISBN: (纸本)1853128066

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CH1, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. the convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. this representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.

关键词： learning systems

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Efficient feature selection for high-dimensional data using two-level filter

Efficient feature selection for high-dimensional data using ...

引用

Proceedings of 2004 international conference on machine learning and Cybernetics

作者： Li, Yun Wu, Zhong-Fu Liu, Jia-Min Tang, Yan-Yun Department of Computer ChongQing University ChongQing 400044 China College of Optics and Electronics ChongQing University ChongQing 400044 China China Light Indust. Mat. Co.Ltd. ChongQing Branch ChongQing 400060 China

ISBN: (纸本)0780384032

Feature selection is a key problem to pattern recognition and machine learning, and it is difficult to get the optimal feature subset for its NP-hand. Currently, the dimensionality of feature set or instance set is very high in many applications, such as information retrieval, so the feature selection from high-dimensional data is also an urgent task for researchers. the paper presents a new approach, which is a two-level filter model system integrateing the ReliefF and a newly developed algorithm of feature cluster, to reduce the dimensionality of large-scale feature set via the feature correlation(relevance) including the feature-feature correlation and feature-class correlation. Our major contributions are: (1) to present a system to perform feature selection from high-dimensional data;(2) to analyze the change of system architecture according to the time cost of the parts in the system;(3) to summarize and comment on the calculations of feature correlation;(4)to perform experiments to show the effective of the proposed approach, which has shown that the system can efficiently get a better compromise between dimensionality reduction and accuracy rate of classification than just part of the system. In many cases, it can improve the accuracy rate and dimensionality reduction.

关键词： data mining

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Collecting and sharing bilingual spontaneous speech corpora: the chinfadial experiment 4

Collecting and sharing bilingual spontaneous speech corpora:...

引用

4th international conference on Language Resources and Evaluation, LREC 2004

作者： Fafiotte, Georges Boitet, Christian Seligman, Mark Zong, Chengqing 385 rue de la Bibliothèque Grenoble Cedex 9F-38041 France National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O.Box 2728 Beijing100080 China

ISBN: (纸本)2951740816

We describe here the three main platforms in the ERIM family of Web-based environments for human interpreting, two of them in more details, ERIM-Interp and ERIM-Collect, then ERIM-Aid. Each platform supports an aspect of the collecting or study of spontaneous bilingual dialogues, translated by an interpreter. ERIM-Interp is the core environment, providing mediated communication between speakers and human interpreters over the network. Using ERIM-Collect, French-Chinese interpreting data have been collected within the 3-year "ChinFaDial" project supported by LIAMA, a French-Chinese laboratory in Beijing. these "raw" speech data will be made available in the spring of 2004 on an open-access basis, using the DistribDial server, on a CLIPSGETA website. Our goal is to extend such corpora, on a collaborative scheme, to allow other research groups to contribute to the site whatever annotations they may have created, and to share them under the same conditions (GPL). An ERIM-Aid variant is intended to provide focused machine AIDS to Web-based human interpreters, or to monolingual distant speakers conversing in different languages.

关键词： Websites

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Relevant linear feature extraction using side-information and unlabeled data

Relevant linear feature extraction using side-information an...

引用

international conference on pattern recognition

作者： Fei Wu Yonglei Zhou Changshui Zhang Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0769521282

"learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant linear feature extraction. It efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. Both good relevant feature extraction and reasonable similarity matrix estimation can be realized. Specifically, we adopt relevant component analysis (RCA) under this framework and get the derived iterative self-enhanced relevant component analysis (ISERCA) algorithm. the experimental results on several data sets show that ISERCA outperforms RCA.

关键词： Feature extraction Iterative algorithms Robustness Algorithm design and analysis Automation data mining Clustering algorithms machine learning machine learning algorithms Principal component analysis

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Knowledge discovery in hepatitis C virus transgenic mice

引用

17th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004

作者： Famili, A. Fazel Ouyang, Junjun Kryworuchko, Marko Alvarez-Maya, Ikuri Smith, Brandon Diaz-Mitoma, Francisco Institute for Information Technology Bldg. M-50 NRC OttawaONK1A0R6 Canada Children’s Hospital of Eastern Ontario 401 Smyth Rd OttawaONK1H 8L1 Canada Institute for Biological Sciences Bldg. M-54 NRC OttawaONK1A0R6 Canada

ISBN: (纸本)3540220070

For the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for useful patterns in the data. the approach involves validation and elimination of irrelevant data, extensive data pre-processing, data visualization, exploratory clustering, pattern recognition and model summarization. We have evaluated our method using data from microarray experiments in a Hepatitis C Virus transgenic mouse model. We demonstrate that from a total of 15311 genes (attributes) we can generate simple models and identify a small number of genes that can be used for future classifications. the approach has potential for future disease classification, diagnostic and virology applications. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data mining

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Neural network-based proper names extraction in fax images

Neural network-based proper names extraction in fax images

引用

international conference on pattern recognition

作者： N. Azzabou L. Likforman-Sulem Signal and Image Processing Laboratory & CNRS LTCI GET/Ecole Nationale Supérieure des Télécommunications Paris France

In this paper, we are interested in the sender's name extraction in fax cover pages through a machine learning scheme. For this purpose, two analysis methods are implemented to work in parallel. the first one is based on image document analysis (OCR recognition, physical block selection), the other on text analysis (word feature extraction, local grammar rules). Our main contribution consisted in introducing a neural network to find an optimal combination of the two approaches. Tests carried on real fax images show that the neural network improves performance compared to an empirical combination function and to each method used separately.

关键词： Neural networks Intelligent networks Image analysis data mining machine learning Text analysis Optical character recognition software Text recognition Feature extraction Electronic mail

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4th international Workshop on Algorithms in Bioinformatics, WABI 2004

引用

4th international Workshop on Algorithms in Bioinformatics, WABI 2004

ISBN: (纸本)3540230181

the proceedings contain 39 papers. the special focus in this conference is on Algorithms in Bioinformatics. the topics include: Reconstructing ancestral bacterial genomes from gene-content and order data;reconstructing ancestral gene orders using conserved intervals;sorting by reversals with common intervals;a polynomial-time algorithm for the matching of crossing contact-map patterns;algorithms for finding maximal-scoring segment sets;gapped local similarity search with provable guarantees;suboptimal local alignments across multiple scoring schemes;adding hidden nodes to gene networks;joint analysis of DNA copy numbers and gene expression levels;searching for regulatory elements of alternative splicing events using phylogenetic footprinting;supervised learning-aided optimization of expert-driven functional protein sequence annotation;solving the protein threading problem by Lagrangian relaxation;recognition of similar spatial and chemical organizations;an algorithmic tool for domain discovery in protein sequences;sequence database compression for peptide identification from tandem mass spectra;a new integer programming formulation for the pure parsimony problem in haplotype analysis;a fast heuristic for single individual SNP haplotype reconstruction;the minisatellite transformation problem revisited;a faster and more space-efficient algorithm for inferring arc-annotations of RNA sequences through alignment;new algorithms for multiple DNA sequence alignment;chaining algorithms for alignment of draft sequence;translation initiation sites prediction with mixture Gaussian models;topological rearrangements and local search method for tandem duplication trees and integrating sample-driven and pattern-driven approaches in motif finding.

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Selecting models from videos for appearance-based face recognition

Selecting models from videos for appearance-based face recog...

引用

international conference on pattern recognition

作者： A. Hadid M. Pietikainen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering University of Oulu Finland

We propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. the approach is based on representing the face manifold in a low-dimensional space using the locally linear embedding (LLE) algorithm and then performing K-means clustering. We define the face models as the cluster centers. Our strategy is motivated by the efficiency of LLE to recover meaningful low-dimensional structures hidden in complex and high dimensional data such as face images. Two other well-known unsupervised learning algorithms (Isomap and SOM) are also considered. We compare and assess the efficiency of these different schemes on the CMU MoBo database which contains 96 face sequences of 24 subjects. the results clearly show significant performance enhancements over traditional methods such as the PCA-based one.

关键词： Face recognition Face detection Clustering algorithms Video sequences Principal component analysis Image databases Image recognition data mining Facial features machine vision

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Video object plane extraction for surveillance applications

Video object plane extraction for surveillance applications

引用

international conference on machine learning and Cybernetics (ICMLC)

作者： Juan Li Xian-Min Zhang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)0780384032

Extraction of video object plane (VOP) is the central challenge for content-based video processing. It enables separate and flexible manipulation, blending and arrangement of content at MPEG-4 based decoder. We present a fast and efficient extraction algorithm depending on edge detection. Our algorithm begins with finding edge candidates detected by Canny edge detector. After selecting edge pixels which belong to the moving object in current frame, the VOP is extracted by using morphological operations. the proposed algorithm is evaluated on MPEG-4 test sequence and generates desirable VOPs at a lower computation cost.

关键词： Image edge detection data mining Noise generators MPEG 4 Standard Morphological operations Video surveillance Intelligent systems Background noise Image processing pattern recognition

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Decision Fusion in Cooperative Adaptive Systems

Decision Fusion in Cooperative Adaptive Systems

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the 8th international conference on Computer Supported Cooperative Work in Design(第八届计算机支持的协同工作设计国际会议)(CSCWD2004)

作者： Mohamed Kamel Pattern Analysis and Machine Intelligence Lab.University of Waterloo Waterloo OntarioCanadaN2L 3G1

Research on cooperative, adaptive intelligentsystems, involves studying, developing and evaluatingarchitectures and methods to solve complex problemsusing adaptive and cooperative systems. these systemsmay range from simple software modules (such as aclustering or a classification algorithm) to physicalsystems (such as autonomous robots, machines orsensors).the main characteristic of these systems is that theyare adaptive and cooperative. By adaptive, it is meantthat the systems have a learning ability that makes themadjust their behaviour or performance to cope withchanging situations. the systems are willing tocooperate together to solve complex problems or toachieve common *** pattern recognition, there are notable contributionson the use of multiple classifiers. the most dominantdecomposition model used is an ensemble of classifiers(identical structures) that are trained differently. Mostof the innovations are in the combining methods. thereare weighting and voting approaches, probabilisticapproaches and approximate and fuzzy logicapproaches. In the area of sensor fusion, there havebeen some interesting ideas for fusing the data anddecisions of the sensors. However, most of thesecombining schemes are usually applied as a postprocessing *** this work are concerned with investigatingarchitectures and methods of aggregating decisions in amulti-classifier or multi-agent environment. Newarchitectures that allow active cooperation will bedeveloped. the classifiers (or agents) have to knowsome knowledge about others in the system. Differentforms of cooperation will be reported. In order for thesearchitectures to allow for dynamic decision fusion, theaggregation procedures have to have the flexibility toadapt to changes in the input and output and adjust toimprove on the final output. Changes will be learned bymeans of extracting features using feature *** of these architectures to problems inclassification of data, distributed data mining a

关键词： Adaptive systems Intelligent sensors Sensor fusion data mining Feature extraction Computer vision Intelligent systems Intelligent robots machine intelligence Computer architecture

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