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共 2835 条记录，以下是2461-2470 订阅

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Visual Tools for Analysing Evolution, Emergence, and Error in data Streams

Visual Tools for Analysing Evolution, Emergence, and Error i...

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international conference on Computer and Information Science (ACIS)

作者： Sol Hart John Yearwood Adil M. Bagirov School of Information Technology and Mathematical Sciences University of Ballarat Mount Helen VIC Australia

the relatively new field of stream mining has necessitated the development of robust drift-aware algorithms that provide accurate, real time, data handling capabilities. Tools are needed to assess and diagnose important trends and investigate drift evolution parameters. In this paper, we present two new and novel visualisation techniques, Pixie and Luna graphs, which incorporate salient group statistics coupled with intuitive visual representations of multidimensional groupings over time. through the novel representations presented here, spatial interactions between temporal divisions can be diagnosed and overall distribution patterns identified. It provides a means of evaluating in non-constrained capacity, commonly constrained evolutionary problems.

关键词： Computer errors Robustness data mining machine learning Computer displays Information analysis Algorithm design and analysis Information technology Australia data handling

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Meta-learning: From Classification to Forecasting, to Optimization, and Beyond

Meta-Learning: From Classification to Forecasting, to Optimi...

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international conference on Computer and Information Science (ACIS)

作者： Kate Smith-Miles Head of School of Engineering and Information Technology Deakin University Australia

Summary form only given: the goal of meta-learning is to model the relationships between the performance of various learning algorithms and the characteristics of problems being learned. In this sense, we are focused on learning about learning. Under what conditions can we expect a certain algorithm to perform well? the field of meta-learning has been very well developed in the machine learning community over the last 15 years or so, where the focus has been on the study of supervised learning methods such as support vector machines and neural networks, and their performance on classification problems. But the goal of seeking a greater understanding of the relationship between problem characteristics and algorithm performance is not limited to machine learning or classification problems. In this talk we will explore the generalisation of meta-learning to other domains including forecasting, optimisation, bioinformatics, etc. the common factor in these diverse fields is the availability of a large number of algorithms for solving the problems, the availability of large benchmark datasets, and the existence of suitable metrics to characterise the properties of the datasets. In each case, great insights into the conditions under which various algorithms performs best can be derived using a meta-learning framework, helping in the design of better algorithms, as well as automated algorithm selection methods.

关键词： data mining machine learning algorithms Information technology Neural networks Magnetic heads machine learning Availability Intelligent systems Computational intelligence Competitive intelligence

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Semantic knowledge in williams syndrome: Insights from comparing behavioural and brain processes in false memory tasks

Semantic knowledge in williams syndrome: Insights from compa...

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IEEE international conference on Development and learning, ICDL

作者： Ching-Fen Hsu Annette Karmiloff-Smith Ovid Tzeng Rung-Tai Chin Hua-Chen Wang Centre for Brain and Cognitive Development Birkbeck University of London UK Centre for Brain and Cognitive Development Birkbeck University of London UK Laboratory for Cognitive Neuroscience National Yang-Ming University Taiwan Laboratory for Cognitive Neuroscience National Yang Ming University Taiwan Department of Linguistics University of Postdam Germany

this study attempts to understand the relationship between use of context and semantic knowledge in the genetic disorder, Williams syndrome (WS).Earlier work had arrived at discrepant results, suggesting either near normal semantic priming [10), or unusual lexical organization [5] and atypical sentence integration [8] in this clinical group. To address these discrepant findings, we used two methodologies with an auditory false memory paradigm, and measured behavioural and neurophysiological (ERP) responses from three groups: children and adults with WS, Mental-Age matched normal children, and normal adults. While the behavioural data suggested that individuals with WS revealed a similar pattern of recognition as both groups of controls for words with semantic relatedness, their neurophysiological correlates suggested a different pattern. Our findings indicate that WS proficient compensatory behaviour camouflages a deviant neural pathway in the use of contextual cues. Our results also point to neurological changes during typical development, since typically developing children showed a distinctive pattern from our adult participants. Overall, our findings suggest that semantic organization develops slowly over typical development, and atypically in the Williams syndrome.

关键词： Testing Delay Laboratories Neuroscience Genetics Enterprise resource planning pattern recognition Neural pathways Autism Recruitment

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Shot Boundary Detection by a Hierarchical Supervised Approach

Shot Boundary Detection by a Hierarchical Supervised Approac...

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international conference on Systems, Signals and Image Processing, IWSSIP

作者： G. Camara-Chavez F. Precioso M. Cord S. Phillip-Foliguet A. de A. Araujo Equipe Traitement des Images et du Signal ENSEA Cergy-Pontoise France DCC Minas Gerais Brazil

Video shot boundary detection plays an important role in video processing. It is the first step toward video-content analysis and content-based video retrieval. We develop a hierarchical approach for shot boundary detection based on the assumption that hierarchy helps to take decisions by reducing the amount of indeterminate transitions. Our method consists in first detecting abrupt transitions using a learning-based approach, then non-abrupt transitions are split into gradual transitions and normal frames. We describe in this paper, a machine learning system for shot boundary detection. the core of this system is a kernel-based SVM classifier. We present some results obtained for shot extraction TRECVID 2006 Task.

关键词： Gunshot detection systems Support vector machines Support vector machine classification Histograms Lighting data mining Motion detection Brightness Content based retrieval Video sequences

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Internetworking security model based on intelligent system

Internetworking security model based on intelligent system

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international conference on Information, Communications and Signal Processing

作者： Surat Srinoy Witcha Chimphlee Department of Computer Science Suan Dusit Rajabhat University Bangkok Thailand

ISBN: (纸本)9781424409822

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. In this paper we present the architecture of our system that combines anomaly and misuse intrusion detection in a hybrid system that tries to take benefit of the best practices of both misuse and anomaly detection approaches. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods.

关键词： Internetworking Intelligent systems Intrusion detection Computer security Computer networks Computer science Electronic mail Information systems machine learning data mining

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Multiscale shape features for classification of bronchovascular anatomy in CT using AdaBoost

Multiscale shape features for classification of bronchovascu...

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Medical Imaging 2007 conference

作者： Ochs, Robert A. Goldin, Jonathan G. Abtin, Fereidoun Kim, Hyun J. Brown, Kathleen Batra, Poonam Roback, Donald McNitt-Gray, Michael F. Brown, Matthew S. Univ Calif Los Angeles Dept Biomed Phys & Radiol 924 Westwood BlvdSuite 650 Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Radiol Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Biostat & Radiol Los Angeles CA 90095 USA

ISBN: (纸本)9780819466303

Lung CAD systems require the ability to classify a variety of pulmonary structures as part of the diagnostic process. the purpose of this work was to develop a methodology for fully automated voxel-by-voxel classification of airways, fissures, nodules, and vessels from chest CT images using a single feature set and classification method. Twenty-nine thin section CT scans were obtained from the Lung Image database Consortium (LIDC). Multiple radiologists labeled voxels corresponding to the following structures: airways (trachea to 6th generation), major and minor lobar fissures, nodules, and vessels (hilum to peripheral), and normal lung parenchyma. the labeled data was used in conjunction with a supervised machine learning approach (AdaBoost) to train a set of ensemble classifiers. Each ensemble classifier was trained to detect voxels part of a specific structure (either airway, fissure, nodule, vessel, or parenchyma). the feature set consisted of voxel attenuation and a small number of features based on the eigenvalues of the Hessian matrix (used to differentiate structures by shape) computed at multiple smoothing scales to improve the detection of both large and small structures. When each ensemble classifier was composed of 20 weak classifiers, the AUC values for the airway, fissure, nodule, vessel, and parenchyma classifiers were 0.984 +/- 0.011, 0.949 +/- 0.009, 0.945 +/- 0.018, 0.953 +/- 0.016, and 0.931 +/- 0.015 respectively.

关键词： pattern recognition segmentation multiscale bronchovascular anatomy AdaBoost Hessian airways fissues nodules vessels

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Gene-finding as an Attribute Selection Task

Gene-finding as an Attribute Selection Task

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international conference on Computer and Information Science (ACIS)

作者： Helyane Bronoski Borges Julio Cesar Nievola PPGIa Pontifícia Universidade Católica do Paraná Curitiba Parana Brazil

For data miners, bioinformatics pose a most demanding challenge than only creating efficient algorithms. they should work with databases that are more "horizontal" than "vertical", as the data consist of a few samples of a large (sometimes huge) number of attributes in the case of micro-arrays. More important is the fact that there is a priori biological knowledge saying that only a few genes are normally linked to each characteristic exhibited by the individual. It allows one to use Attribute Selection to determine which attributes are more likely to induce the observable characteristic. In this paper a study on many configurations of attribute selection schemes is made on two typical bioinformatics datasets. the results show that sequential subset generation guarantees better results and reiterates the use of the wrapper approach to achieve better classification, despite its running time being larger than the filter approach.

关键词： Cancer data mining databases Genetics Bioinformatics machine learning DNA Filters Diseases Malignant tumors

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SME: learning Automata-Based Algorithm for Estimating the Mobility Model of Soccer Players

SME: Learning Automata-Based Algorithm for Estimating the Mo...

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IEEE international conference on Cognitive Informatics

作者： A. H. Jamalian A. R. Sefidpour M. T. Manzuri-Shalmani R. Iraji Department of Computer Engineering Electrical Engineering Sharif University of Technology Tehran Iran Card Department Bank Eghtesad Novin Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran

Soccer model and relation of players and coach has been analyzed by a learning automata-based method, called soccer mobility estimator (SME), who estimates the mobility model of soccer players. During a soccer match, players play according to a certain program designed by coach. the pattern of players' mobility is not stochastic and it can be assumed that they are playing with a certain mobility model. Since knowledge about mobility model of nodes in mobile ad-hoc networks has a substantial effect on its performance evaluation, knowledge about mobility model of soccer players can be useful for coaches and experts for game analysis. In fact the mobility model of players could be an important parameter for assessment of team solidarity. Simulation results show that the mobility model of soccer players is similar, up to 66%, to the RPGM (reference point group mobility) mobility model.

关键词： learning automata Games Histograms Solid modeling Target tracking Robustness Computer vision Cameras Video sequences data mining

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Toward Building Lightweight Intrusion Detection System through Modified RMHC and SVM

Toward Building Lightweight Intrusion Detection System Throu...

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IEEE international conference on Networks

作者： You Chen Wen-Fa Li Xue-Qi Cheng Graduate University Chinese Academy and Sciences China Chinese Academy of Sciences Beijing Beijing CN Institute of Computing Technology Chinese Academy and Sciences China

ISBN: (纸本)9781424412297;1424412293

Feature selection attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we present a wrapper-based feature selection algorithm aiming at modeling lightweight intrusion detection system (IDS) by (1) using modified random mutation hill climbing (MRMHC) as search strategy to specify a candidate subset for evaluation; (2) using support vector machines (SVMs) as wrapper approach to obtain the optimum feature subset. We have examined the feasibility of our feature selection algorithm by conducting several experiments on KDD 1999 intrusion detection dataset which was categorized as DOS, PROBE, R2L and U2R. the experimental results show that our approach is able not only to speed up the process of selecting important features but also to guarantee high detection rates. Furthermore, our experiments indicate that intrusion detection system with a combination of our proposed approach has smaller computational resources than that with GA-SVM which is a popular feature selection algorithm in the field.

关键词： Intrusion detection Support vector machines Filters Computational complexity machine learning algorithms Genetic algorithms Support vector machine classification Accuracy Computers Optimization methods

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machine learning and data mining in pattern recognition - 4th international conference, MLDM 2005, Proceedings

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4th international conference on machine learning and data mining in pattern recognition, MLDM 2005

ISBN: (纸本)3540269231

the proceedings contain 68 papers. the topics discussed include: incremental classification rules based on association rules using formal concept analysis;finite mixture models with negative components;principles of multi-kernel data mining;a comprehensible SOM-based scoring system;linear manifold clustering;clustering document images using graph summaries;unsupervised learning of visual feature hierarchies;a new multidimensional feature transformation for linear classifiers and its applications;embedding time series data for classification;statistical supports for frequent itemsets on data streams;neural expert model applied to phonemes recognition;and signature-based approach for intrusion detection.

关键词： data mining

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