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international conference on pattern recognition

作者： Palaiahnakote Shivakumara Deepu Rajan Suresh A. Sadananthan School of Computing National University of Singapore Singapore School of Computer Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424421749

In this paper, we investigate if rule-based systems are useful for image classification problems when the number of classes is fixed. the rules are derived from simple edge features such as width and straightness. A class representative is calculated for each class according to the average percentage of edges that satisfy the rule for a particular class. this percentage for an unknown image is compared to the class representative to assign a label to it. the proposed system does not require extensive feature extraction and classification techniques. It is shown that the rule based system outperforms some of the reported results on scene classification.

关键词： Image classification Knowledge based systems Layout Image edge detection Image retrieval Feature extraction machine learning Road transportation data mining Information retrieval

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Embedding HMM’s-based models in a Euclidean space: the topological hidden Markov models

Embedding HMM’s-based models in a Euclidean space: The topo...

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international conference on pattern recognition

作者： Djamel Bouchaffra Grambling State University

ISBN: (纸本)9781424421749

One of the major limitations of HMM-based models is the inability to cope with topology: when applied to a visible observation (VO) sequence, HMM-based techniques have difficulty predicting the n-dimensional shape formed by the symbols of the VO sequence. To fulfill this need, we propose a novel paradigm named "topological hidden Markov models" (thMM's) that classifies VO sequences by embedding the nodes of an HMM state transition graph in a Euclidean space. We have applied the concept of thMM's to: (i) predict the ASCII class assigned to a handwritten numeral, and (ii) map a protein primary structure to its 3D fold. the results show that the concept of second level thMM's outperforms the SHMM's and the SVM classifiers.

关键词： Hidden Markov models Shape Topology Speech recognition Predictive models machine learning data mining Signal analysis Proteins Support vector machines

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learning a discriminative sparse tri-value transform

Learning a discriminative sparse tri-value transform

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international conference on pattern recognition

作者： Zhenhua Qu Guoping Qiu Pong C. Yuen School of Information Science and Technology Sun Yat-sen University China School of Computer Science University of Nottingham UK Department of Computer Science Hong Kong Baptist University Hong Kong China

ISBN: (纸本)9781424421749

Simple binary patterns have been successfully used for extracting feature representations for visual object classification. In this paper, we present a method to learn a set of discriminative tri-value patterns for projecting high dimensional raw visual inputs into a low dimensional subspace for tasks such as face detection. Unlike previous methods that use predefined simple transform bases to generate tens of thousands features first and then use machine learning to select the most useful features, our method attempts to learn discriminative transform bases directly. Since it would be extremely hard to develop analytical solutions, we define an objective function that can be solved using simulated annealing. To reduce the search space, we impose sparseness and smoothness constraints on the transform bases. Experimental results demonstrate that our method is effective and provides an alternative approach to effective visual object classification.

关键词： Feature extraction Independent component analysis Computer science Face detection Simulated annealing Information science Sun data mining Analytical models pattern recognition

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learning invariant region descriptor operators with genetic programming and the F-measure

Learning invariant region descriptor operators with genetic ...

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international conference on pattern recognition

作者： Cynthia B. Perez Gustavo Olague CICESE Research Center Centro de Investigación Científica y de Educación Superior de Ensenada Ensenada Baja California Mexico

ISBN: (纸本)9781424421749

Recognizing and localizing objects is a classical problem in computer vision that is an important stage for many automated systems. In order to perform object recognition many researchers have focused on local features as the basis of their proposed methodologies. this work is devoted to the task of learning invariant region descriptor operators with genetic programming. the idea is to find a set of expressions that could be equal or better than the weighted gradient magnitude that is normally applied on the SIFT descriptor. this magnitude corresponds to the operator that we would like to improve through genetic programming (GP). the key for a successful problem statement was achieved with the F-measure. After a bibliographical study we have found a criterion that is simple, reliable, and useful in the estimation of such a metric. the measure that we propose here is based on the harmonic mean which is normally used by the information retrieval community. Experimental results show that the evolved descriptor¿s operator can enhance significantly the overall performance of the SIFT descriptor and surpass other state-of-the-art algorithms.

关键词： Genetic programming Object recognition Histograms Computer vision Information retrieval Principal component analysis Veins data mining machine vision Image matching

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Private data discovery for privacy compliance in collaborative environments

Private data discovery for privacy compliance in collaborati...

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5th international conference on Cooperative Design, Visualization, and Engineering, CDVE 2008

作者： Korba, Larry Wang, Yunli Geng, Liqiang Song, Ronggong Yee, George Patrick, Andrew S. Buffett, Scott Liu, Hongyu You, Yonghua Institute for Information Technology National Research Council of Canada Building M-50 Montreal Road Ottawa ON K1A 0R6 Canada

ISBN: (纸本)3540880100

With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). this problem becomes even more evident in collaborative computing environments. PII may be hidden anywhere within the file system of a computer. As well, in the course of different activities, via collaboration or not, personally identifiable information may migrate from computer to computer. this makes meeting the organizational privacy requirements all the more complex. Our particular interest is to develop technology that would automatically discover workflow across organizational collaborators that would include private data. Since in this context, it is important to understand where and when the private data is discovered, in this paper, we focus on PII discovery, i.e. automatically identifying private data existant in semi-structured and unstructured (free text) documents. the first part of the process involves identifying PII via named entity recognition. the second part determines relationships between those entities based upon a supervised machine learning method. We present test results of our methods using publicly-available data generated from different collaborative activities to provide an assessment of scalability in cooperative computing environment. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： data mining

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Pre-processing aspects for complexity reduction of the QSAR problem

Pre-processing aspects for complexity reduction of the QSAR ...

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international IEEE conference on Intelligent Systems, IS

作者： L. Dumitriu C. Segal M-V. Craciun A. Cocu Compute Science Department Dunarea de Jos University of Galati Galati Romania the Computer Science Department Galati Romania

Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biological and toxicological processes. In real PT problems there is a very important topic to be considered: the huge number of the chemical descriptors. Irrelevant, redundant, noisy and unreliable data have a negative impact, therefore one of the main goals in KDD is to detect these undesirable proprieties and to eliminate or correct them. this assumes data cleaning, noise reduction and feature selection because the performance of the applied machine learning algorithms is strongly related with the quality of the data used. In this paper, we present some of the issues that can be taken into account for preparing data before the actual knowledge discovery is performed.

关键词： Chemical compounds data mining Toxicology pattern recognition Principal component analysis databases Noise reduction Neural networks Statistical analysis pattern analysis

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Training Linear Discriminant Analysis in Linear Time

Training Linear Discriminant Analysis in Linear Time

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IEEE 24th international conference on data Engineering (ICDE 2008), vol.1

作者： Deng Cai Xiaofei He Jiawei Han Department of Computer Science University of Illinois Urbana IL USA Yahoo Inc. Burbank CA USA

ISBN: (纸本)9781424418367;1424418364

Linear Discriminant Analysis (LDA) has been a popular method for extracting features which preserve class separability. It has been widely used in many fields of information processing, such as machine learning, data mining, information retrieval, and pattern recognition. However, the computation of LDA involves dense matrices eigen-decomposition which can be computationally expensive both in time and memory. Specifically, LDA has O(mnt + t{sup}3) time complexity and requires O(mn + mt + nt) memory, where m is the number of samples, n is the number of features and t = min(m,n). When both m and n are large, it is infeasible to apply LDA. In this paper, we propose a novel algorithm for discriminant analysis, called Spectral Regression Discriminant Analysis (SRDA). By using spectral graph analysis, SRDA casts discriminant analysis into a regression framework which facilitates both efficient computation and the use of regularization techniques. Our theoretical analysis shows that SRDA can be computed with O(ms) time and O(ms) memory, where s(≤ n) is the average number of non-zero features in each sample. Extensive experimental results on four real world data sets demonstrate the effectiveness and efficiency of our algorithm.

关键词： laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Linear discriminant analysis discriminant analysis Information Processing information retrieval Memory Memory dataset pattern recognition theoretical analysis

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A machine learning methodology for the analysis of workplace accidents

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international JOURNAL OF COMPUTER MAthEMATICS 2008年第3-4期85卷 559-578页

作者： Matias, J. M. Rivas, T. Martin, J. E. Taboada, J. Univ Vigo Dept Stat Vigo 36310 Spain Univ Vigo Dept Nat Resources Vigo 36310 Spain

this article proposes a methodology for the analysis of the causes and types of workplace accidents (in this paper we focus specifically on floor-level falls). the approach is based on machine learning techniques: Bayesian networks trained using different algorithms (with and without a priori information), classification trees, support vector machines and extreme learning machines. the results obtained using the different techniques are compared in terms of explanatory capacity and predictive potential, both factors facilitating the development of risk prevention measures. Bayesian networks are revealed to be the best all-round technique for this type of study, as they combine a powerful interpretative capacity with a predictive capacity that is comparable to that of the best available techniques. Moreover, the Bayesian networks force experts to apply a scientific approach to the construction and progressive enrichment of their models and also enable the basis to be laid for an accident prevention policy that is solidly grounded. Furthermore, the procedure enables better variable definition, better structuring of the data capture, coding, and quality control processes.

关键词： machine learning statistics operations research data mining artificial intelligence

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A statistical algorithm to discover knowledge in medical data sources

A statistical algorithm to discover knowledge in medical dat...

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6th international conference on machine learning and Applications

作者： Senf, Alexander J. Leonard, Carl DeLeo, James Univ Kansas Lawrence KS 66045 USA Natl Inst Hlth Bethesda MD 20892 USA

ISBN: (纸本)9780769530697

Developing intelligent tools to extract information from data collections has long been of critical importance in fields such as knowledge discovery, information retrieval, pattern recognition, and databases. With the advent of electronic medical records and medical data repositories there is new potential to apply these techniques to the analysis of biomedical data sets. Looking for complex patterns within large biomedical data repositories and discovering previously unexpected associations can be of particular interest for understanding the physiology and functionality of the human body as well as tracing the roots of diseases. In the context of a research hospital these analyses may lead to further directed research, better diagnostic capabilities, and improved patient outcomes. this paper describes an implementation of a knowledge discovery algorithm aimed at such data sets.

关键词： clustering methods pattern recognition unsupervised learning feature extraction

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Feature selection by combining fisher criterion and principal feature analysis.

Feature selection by combining fisher criterion and principa...

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6th international conference on machine learning and Cybernetics

作者： Wang, Sa Liu, Cheng-Lin Zheng, Lian Beijing Inst Technol Sch Aerosp Sci & Engn Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China

ISBN: (纸本)9781424409723

Feature selection is one of the most important issues in the fields such as data mining, pattern recognition and machine learning. In this study, a new feature selection approach that combines the Fisher criterion and principal feature analysis (PFA) is proposed in order to identify the important (relevant and irredundant) feature subset. the Fisher criterion is used to remove features that are noisy or irrelevant, and then PFA is used to choose a subset of principal features. the proposed approach was evaluated in pattern classification on five publicly available datasets. the experimental results show that the proposed approach can largely reduce the feature dimensionality with little loss of classification accuracy.

关键词： feature selection fisher criterion principal feature analysis (PFA) pattern classification

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