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共 3137 条记录，以下是2911-2920 订阅

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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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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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Selecting models from videos for appearance-based face recognition

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

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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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Decision Fusion in Cooperative Adaptive Systems

Decision Fusion in Cooperative Adaptive Systems

引用

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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Proceedings - 5th international conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

Proceedings - 5th International Conference on Computational ...

引用

5th international conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003

ISBN: (纸本)0769519571

the proceedings contain 80 papers. the topics discussed include: intelligent target recognition based on hybrid support vector machine;a maximum likelihood function approach to direction estimation based on evolutionary algorithms;improved decomposition method for support vector machines;face recognition using self-organizing feature maps and support vector machines;support vector machine classifications for microarray expression data set;intelligent encoding of concepts in web document retrieval;model of web oriented intelligent tutoring system for distance education;web mining research;web mining based on multi-agents;study on knowledge processing techniques in air defense operation intelligent aid decision;face detection and facial feature extraction in color image;similarity measure learning for image retrieval using feature subspace analysis;and image retrieval based on multiple features using wavelet.

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learning pedestrian models for silhouette refinement

Learning pedestrian models for silhouette refinement

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9th IEEE international conference on Computer Vision

作者： Lee, L Dalley, G Tieu, K MIT Cambridge MA 02139 USA

ISBN: (纸本)0769519504

We present a model-based method for accurate extraction of pedestrian silhouettes from video sequences. Our approach is based on two assumptions, 1) there is a common appearance to all pedestrians, and 2) each individual looks like him/herself over a short amount of time. these assumptions allow us to learn pedestrian models that encompass both a pedestrian population appearance and the individual appearance variations. Using our models, we are able to produce pedestrian silhouettes that have fewer noise pixels and missing parts. We apply our silhouette extraction approach to the NIST gait data set and show that under the gait recognition task, our model-based sulhouettes result in much higher recognition rates than silhouettes directly extracted from background subtraction, or any non-model-based smoothing schemes.

关键词： data mining

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5th international Symposium on Intelligent data Analysis, IDA 2003

引用

5th international Symposium on Intelligent data Analysis, IDA 2003

ISBN: (纸本)3540408134

the proceedings contain 56 papers. the special focus in this conference is on machine learning, Probability and Topology. the topics include: Pruning for monotone classification trees;regularized learning with flexible constraints;learning to answer emails;a semi-supervised method for learning the structure of robot environment interactions;using domain specific knowledge for automated modeling;resolving rule conflicts with double induction;a novel partial-memory learning algorithm based on grey relational structure;constructing hierarchical rule systems;text categorization using hybrid multiple model schemes;learning dynamic bayesian networks from multivariate time series with changing dependencies;topology and intelligent data analysis;coherent conditional probability as a measure of information of the relevant conditioning events;very predictive ngrams for space-limited probabilistic models;learning linear classifiers sensitive to example dependent and noisy costs;an effective associative memory for pattern recognition;similarity based classification;numerical attributes in decision trees;similarity-based neural networks for applications in computational molecular biology;combining pairwise classifiers with stacking and adapting association rule learning to subgroup discovery.

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An improved Branch & Bound algorithm in feature selection 9th

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9th international conference on Rough Sets, Fuzzy Sets, data mining and Grandular Computing (RSFDGrC 2003)

作者： Wang, ZX Yang, J Li, GZ Shanghai Jiao Tong Univ Inst Pattern Recognit & Image Proc Shanghai 200030 Peoples R China

ISBN: (纸本)3540140409

the Branch & Bound (B&B) algorithm is a globally optimal feature selection method. the high computational complexity of this algorithm is a well-known problem. the B&B algorithm constructs a search tree, and then searches for the optimal feature subset in the tree. Previous work on the B&B algorithm was focused on how to simplify the search tree in order to reduce the search complexity. Several improvements have already existed. A detailed analysis of basic B&B algorithm and existing improvements is given under a common framework in which all the algorithms are compared. Based on this analysis, an improved B&B algorithm, BBPP+, is proposed. Experimental comparison shows that BBPP+ performs best.

关键词： branch & bound feature selection minimum solution tree global optimum machine learning data mining pattern recognition

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Classifying large data sets using SVMs with hierarchical clusters 03

Classifying large data sets using SVMs with hierarchical clu...

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9th ACM SIGKDD international conference on Knowledge Discovery and data mining, KDD '03

作者： Yu, Hwanjo Yang, Jiong Han, Jiawei Department of Computer Science University of Illinois Urbana-Champaign IL 61801 United States

ISBN: (纸本)9781581137378

Support vector machines (SVMs) have been promising methods for classification and regression analysis because of their solid mathematical foundations which convery several salient properties that other methods hardly provide. However, despite the prominent properties of SVMs, they are not as favored for large-scale data mining as for pattern recognition or machine learning because the training complexity of SVMs is highly dependent on the size of a data set. Many real-world data mining applications involve millions or billions of data records where even multiple scans of the entire data are too expensive to perform. this paper presents a new method, Clustering-Based SVM (CB-SVM), which is specifically designed for handling very large data sets. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. CB-SVM tries to generate the best SVM boundary for very large data sets given limited amount of resources. Our experiments on synthetic and real data sets show that CB-SVM is highly scalable for very large data sets while also generating high classification accuracy. Copyright 2003 ACM.

关键词： Support vector machines

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High performance data mining

引用

5th international conference on High Performance Computing for Computational Science, VECPAR 2002

作者： Kumar, Vipin Joshi, Mahesh V. Han, Eui-Hong Sam Tan, Pang-Ning Steinbach, Michael University of Minnesota 4-192 EE/CSci Building 200 Union Street SE MinneapolisMN55455 United States

ISBN: (纸本)3540008527

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. data mining, an important step in this process of knowledge discovery, consists of methods that discover interesting, non-trivial, and useful patterns hidden in the data[SAD+93, CHY96]. the field of data mining builds upon the ideas from diverse fields such as machine learning, pattern recognition, statistics, database systems, and data visualization. But, techniques developed in these traditional disciplines are often unsuitable due to some unique characteristics of today’s data-sets, such as their enormous sizes, high-dimensionality, and heterogeneity. there is a necessity to develop effective parallel algorithms for various data mining techniques. However, designing such algorithms is challenging, and the main focus of the paper is a description of the parallel formulations of two important data mining algorithms: discovery of association rules, and induction of decision trees for classification. We also briefly discuss an application of data mining to the analysis of large data sets collected by Earth observing satellites that need to be processed to better understand global scale changes in biosphere processes and patterns. © Springer-Verlag Berlin Heidelberg 2003.

关键词： data mining

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