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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3350 条记录，以下是2601-2610 订阅

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An Efficient Programming Rule Extraction and Detection of Violations in Software Source Code using Neural networks

An Efficient Programming Rule Extraction and Detection of Vi...

引用

4th international conference on Advanced Computing (ICoAC)

作者： Pravin, A. Srinivasan, S. Sathyabama Univ Chennai Tamil Nadu India Anna Univ Technol Madurai India

ISBN: (纸本)9781467355834;9781467355827

the larger size and complexity of software source code builds many challenges in bug detection. data mining based bug detection methods eliminate the bugs present in software source code effectively. Rule violation and copy paste related defects are the most concerns for bug detection system. Traditional data mining approaches such as frequent Itemset mining and frequent sequence mining are relatively good but they are lacking in accuracy and pattern recognition. Neural networks have emerged as advanced data mining tools in cases where other techniques may not produce satisfactory predictive models. the neural network is trained for possible set of errors that could be present in software source code. From the training data the neural network learns how to predict the correct output. the processing elements of neural networks are associated with weights which are adjusted during the training period.

关键词： Defect Detection data mining Programming Rule Neural Networks Association Rules Decision Trees

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A new learning strategy of general BAMs 1

引用

8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Hoa, Nong thi Duy, Bui the Human Machine Interaction Laboratory Vietnam National University Hanoi Viet Nam

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Bi-directional Associative Memory (BAM) is an artificial neural network that consists of two Hopfield networks. the most important advantage of BAM is the ability to recall a stored pattern from a noisy input, which depends on learning process. Between two learning types of iterative learning and non-iterative learning, the former allows better noise tolerance than the latter. However, interactive learning BAMs take longer to learn. In this paper, we propose a new learning strategy that assures our BAM converges in all states, which means that our BAM recalls perfectly all learning pairs. Moreover, our BAM learns faster, more flexibility and tolerates noise better. In order to prove the effectiveness of the model, we have compared our model to existing ones by theory and by experiments. © 2012 Springer-Verlag.

关键词： Hopfield neural networks

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A pattern recognition system for malicious PDF files detection 1

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8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Maiorca, Davide Giacinto, Giorgio Corona, Igino University of Cagliari Piazza d'Armi 09123 Cagliari Italy

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Malicious PDF files have been used to harm computer security during the past two-three years, and modern antivirus are proving to be not completely effective against this kind of threat. In this paper an innovative technique, which combines a feature extractor module strongly related to the structure of PDF files and an effective classifier, is presented. this system has proven to be more effective than other state-of-the-art research tools for malicious PDF detection, as well as than most of antivirus in commerce. Moreover, its flexibility allows adopting it either as a stand-alone tool or as plug-in to improve the performance of an already installed antivirus. © 2012 Springer-Verlag.

关键词： pattern recognition

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Proximity-graph instance-based learning, support vector machines, and high dimensionality: An empirical comparison 1

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8th international conference on machine learning and data mining in pattern recognition, MLDM 2012

作者： Toussaint, Godfried T. Berzan, Constantin Faculty of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Tufts University Medford MA 02155 United States

ISBN: (数字)9783642315374

ISBN: (纸本)9783642315367

Previous experiments with low dimensional data sets have shown that Gabriel graph methods for instance-based learning are among the best machine learning algorithms for pattern classification applications. However, as the dimensionality of the data grows large, all data points in the training set tend to become Gabriel neighbors of each other, bringing the efficacy of this method into question. Indeed, it has been conjectured that for high-dimensional data, proximity graph methods that use sparser graphs, such as relative neighbor graphs (RNG) and minimum spanning trees (MST) would have to be employed in order to maintain their privileged status. Here the performance of proximity graph methods, in instance-based learning, that employ Gabriel graphs, relative neighborhood graphs, and minimum spanning trees, are compared experimentally on high-dimensional data sets. these methods are also compared empirically against the traditional k-NN rule and support vector machines (SVMs), the leading competitors of proximity graph methods. © 2012 Springer-Verlag.

关键词： Support vector machines

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Construction data mining information management system based on FCA and ontology 1

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2011 international conference on Electronic Engineering, Communication and Management, EECM 2011

作者： Sun, XiuYing Modern Education Technology Center Huanghe Science and Technology College Zhengzhou 450063 Henan China

ISBN: (数字)9783642272875

ISBN: (纸本)9783642272868

Ontology is a formal, explicit specification of a shared conceptual model and provides a way for computers to exchange, search and identify characteristics. data mining is a drawing work from areas including database technology, machine learning, statistics, pattern recognition, neural networks, artificial intelligence, and data visualization. As a branch of applied mathematics, formal concept analysis comes of the understanding of concept in philosophical domain. Finally, data mining information management system in E- Commerce is proposed based on integrating of ontology and formal concept analysis. the experimental results indicate that this method has great promise. © 2012 Springer-Verlag.

关键词： data mining

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Adaptation of the user navigation scheme using clustering and frequent pattern mining techiques for profiling

Adaptation of the user navigation scheme using clustering an...

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4th international conference on Knowledge Discovery and Information Retrieval, KDIR 2012

作者： Arbelaitz, Olatz Gurrutxaga, Ibai Lojo, Aizea Muguerza, Javier Perez, Jesus M. Perona, Inigo Dept. of Computer Architecture and Technology University of Basque Country UPV-EHU M. Lardizabal 1 20018 Donostia Spain

ISBN: (纸本)9789898565297

there is a need to facilitate access to the required information in the web and adapting it to the users' preferences and requirements. this paper presents a system that, based on a collaborative filtering approach, adapts the web site to improve the browsing experience of the user: it generates automatically interesting links for new users. the system only uses the web log files stored in any web server (common log format) and builds user profiles from them combining machine learning techniques with a generalization process for data representation. these profiles are later used in an exploitation stage to automatically propose links to new users. the paper examines the effect of the parameters of the system on its final performance. Experiments show that the designed system performs efficiently in a database accessible from the web and that the use of a generalization process, specificity in profiles and the use of frequent pattern mining techniques benefit the profile generation phase, and, moreover, diversity seems to help in the exploitation phase. Copyright © 2012 SciTePress - Science and Technology Publications.

关键词： Collaborative filtering

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Comparative analysis of machine learning techniques for the prediction of the DMPK parameters intrinsic clearance and plasma protein binding

Comparative analysis of machine learning techniques for the ...

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4th international conference on Bioinformatics and Computational Biology 2012, BICoB 2012

作者： Lowe Jr., Edward W. Butkiewicz, Mariusz White III, Zollie Spellings, Matthew Omlor, Albert Meiler, Jens Department of Chemistry Vanderbilt University Nashville TN 37235 United States

ISBN: (纸本)9781618397461

Several machine learning techniques were evaluated for the prediction of parameters relevant in pharmacology and drug discovery including rat and human microsomal intrinsic clearance as well as plasma protein binding represented as the fraction of unbound compound. the algorithms assessed in this study include artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, kappa nearest neighbor (KNN), and Kohonen Networks. the data sets, obtained through literature data mining, were described through a series of scalar, two- and three-dimensional descriptors including 2-D and 3-D autocorrelation, and radial distribution function. the feature sets were optimized for each data set individually for each machine learning technique using sequential forward feature selection. the data sets range from 400 to 600 compounds with experimentally determined values. Intrinsic clearance (CL int) is a measure of metabolism by cytochrome P-450 enzymes primarily in the vesicles of the smooth endoplasmic reticulum. these important enzymes contribute to the metabolism of an estimated 75% of the most frequently prescribed drugs in the U.S. the fraction of unbound compound (fu) greatly influences pharmacokinetics, efficacy, and toxicology. In this study, machine learning models were constructed by systematically optimizing feature sets and algorithmic parameters to calculate these parameters of interest with cross validated correlation/RMSD values reaching 9.53 over the normalized data set. these fully in silico models are useful in guiding early stages of drug discovery, such as analogue prioritization prior to synthesis and biological testing while reducing costs associated with the in vitro determination of these parameters. these models are made freely available for academic use.

关键词： Neural networks

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Classification of sole patterns from a 3-Dimensional shoe model

Classification of sole patterns from a 3-Dimensional shoe mo...

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4th international conference on machine Vision (ICMV) - Computer Vision and Image Analysis - pattern recognition and Basic Technologies

作者： Zhao, Qingmin Dong, Feifei Wu, Jianhua Cheng, Jian Zhu, Yican Nanchang Univ Elect & Informat Engn Dept Nanchang Jiangxi Peoples R China Jiangxi Prov Publ Secur Bur Ctr Forens Inst Nanchang Jiangxi Peoples R China

ISBN: (纸本)9780819490261

the purpose of this paper is to classify the sole patterns from a 3D shoe model which is comprised of scattered point cloud data. Sole patterns can be divided into five categories based on the texture of each pattern. the point cloud data is sliced into a number of layers, and the unordered data points in each layer are projected onto a viewing plane to get a 2D shoeprint, in which we can further segment a texture element by region growing. then, each texture element segmented can be classified into two types, non-closed curve and closed curve, by detecting if there are point cloud data in each external unit of the region and looking for the nearest points to the region. Finally, we can identify the type of the texture element into one of the five categories by analyzing its geometrical characteristics.

关键词： sole pattern point cloud data closed curve region growing classification

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Cumulative restricted boltzmann machines for ordinal matrix data analysis

Cumulative restricted boltzmann machines for ordinal matrix ...

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4th Asian conference on machine learning, ACML 2012

作者： Tran, Truyen Phung, Dinh Venkatesh, Svetha Pattern Recognition and Data Analytics Deakin University Waurn Ponds VIC 3216 Australia Department of Computing Curtin University Bentley WA 6102 Australia

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. this paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. the model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments. © 2012 T. Tran, D. Phung & S. Venkatesh.

关键词： Matrix algebra

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Improving Active learning with Sharp data Reduction

Improving Active Learning with Sharp Data Reduction

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20th international conference in Central Europe on Computer Graphics, Visualization and Computer Vision

作者： Saito, Priscila T. M. de Rezende, Pedro J. Falcao, Alexandre X. Suzuki, Celso T. N. Gomes, Jancarlo F. Univ Campinas UNICAMP Inst Comp Campinas SP Brazil Univ Campinas UNICAMP Inst Biol Campinas SP Brazil

ISBN: (纸本)9788086943794

Statistical analysis and pattern recognition have become a daunting endeavour in face of the enormous amount of information in datasets that have continually been made available. In view of the infeasibility of complete manual annotation, one seeks active learning methods for data organization, selection and prioritization that could help the user to label the samples. these methods, however, classify and reorganize the entire dataset at each iteration, and as the datasets grow, they become blatantly inefficient from the user's point of view. In this work, we propose an active learning paradigm which considerably reduces the non-annotated dataset into a small set of relevant samples for learning. During active learning, random samples are selected from this small learning set and the user annotates only the misclassified ones. A training set with new labelled samples increases at each iteration and improves the classifier for the next one. When the user is satisfied, the classifier can be used to annotate the rest of the dataset. To illustrate the effectiveness of this paradigm, we developed an instance based on the optimum path forest (OPF) classifier, while relying on clustering and classification for the learning process. By using this method, we were able to iteratively generate classifiers that improve quickly, to require few iterations, and to attain high accuracy while keeping user involvement to a minimum. We also show that the method provides better accuracies on unseen test sets with less user involvement than a baseline approach based on the OPF classifier and random selection of training samples from the entire dataset.

关键词： pattern recognition machine learning Active learning Semi-Automatic dataset Annotation data mining Optimum-Path Forest Classifiers

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