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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2671-2680 订阅

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Estimation of Parameters of the Empirically Reconstructed Fuzzy Model of Measurements

Estimation of Parameters of the Empirically Reconstructed Fu...

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13th international conference on Rough Sets, Fuzzy Sets and Granular Computing (RSFDGrC) was Co-Organized with the 4th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Kopit, Tatiana Chulichkov, Alexey Moscow MV Lomonosov State Univ Fac Phys Dept Comp Methods Phys Moscow 119991 Russia

ISBN: (纸本)9783642218804

In this paper we introduce a method for the empirical reconstruction of a fuzzy model of measurements on the basis of testing measurements using a possibility-theoretical approach. the method of measurement reduction is developed for solving a problem of an estimation of parameters of a fuzzy system. It is shown that such problems are reduced to minimax problems. If the model is unknown it can be restored from testing experiments and can be applied for handling the problems of the type of forecasting the behavior of a system.

关键词： mathematical modeling fuzzy sets decision making analysis and interpretation of data measurement and computing systems

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learning Readers' News Preferences with Support Vector machines

Learning Readers' News Preferences with Support Vector Machi...

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10th international conference on Artificial Neural Networks and Genetic Algorithms

作者： Hensinger, Elena Flaounas, Ilias Cristianini, Nello Univ Bristol Intelligent Syst Lab Bristol BS8 1TH Avon England

ISBN: (纸本)9783642202667

We explore the problem of learning and predicting popularity of articles from online news media. the only available information we exploit is the textual content of the articles and the information whether they became popular by users clicking on them or not. First we show that this problem cannot be solved satisfactorily in a naive way by modelling it as a binary classification problem. Next, we cast this problem as a ranking task of pairs of popular and non-popular articles and show that this approach can reach accuracy of up to 76%. Finally we show that prediction performance can improve if more content-based features are used. For all experiments, Support Vector machines approaches are used.

关键词： pattern recognition data mining Applications machine learning

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Automatic disease diagnosis systems using pattern recognition based genetic algorithm and neural networks

Automatic disease diagnosis systems using pattern recognitio...

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11th WSEAS international conference on AIC'11, 4th WSEAS international conference on BEBI'11, international conference on Environment, Economics, Energy, Devices, Systems, Communications, Computers, Pure and Applied Mathematics

作者： Adeli, Mahdieh Zarabadipour, Hassan Engineering Department Imam Khomeini International University Iran

ISBN: (纸本)9781618040282

this paper presents three disease diagnosis systems using pattern recognition based on genetic algorithm and neural networks. All systems deal with feature selection and classification. Genetic algorithm chooses subsets of features for the input of the classifier (neural network) and the accuracy of the classifier determine the percentage of effectiveness of each subsets of features. the classifiers using in this paper are general regression neural network (GRNN), radial basis function (RBF) and radial basis network exact fit (RBEF). We use breast cancer and hepatitis disease datasets taken from UCI machine learning database as medical dataset. the system performances are estimated by classification accuracy and they are compared with similar methods without feature selection.

关键词： Genetic algorithms

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Discovering Correspondences between Fingerprints Based on the Temporal Dynamics of Eye Movements from Experts

Discovering Correspondences between Fingerprints Based on th...

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4th international Workshop on Computational Forensics

作者： Yu, Chen Busey, thomas Vanderkolk, John Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9783642193750

Latent print examinations involve a process by which a latent print, often recovered from a crime scene, is compared against a known standard or sets of standard prints. Despite advances in automatic fingerprint recognition, latent prints are still examined by human expert primarily due to the poor image quality of latent prints. the aim of the present study is to better understand the perceptual and cognitive processes of fingerprint practices as implicit expertise. Our approach is to collect fine-grained gaze data from fingerprint experts when they conduct a matching task between two prints. We then rely on machine learning techniques to discover meaningful patterns from their eye movement data. As the first steps in this project, we compare gaze patterns from experts with those obtained from novices. Our results show that experts and novices generate similar overall gaze patterns. However, a deeper data analysis using machine translation reveals that experts are able to identify more corresponding areas between two prints within a short period of time.

关键词： fingerprint Cognitive and Behavioral Studies Computational data analysis data mining

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Simultaneous Non-gaussian data Clustering, Feature Selection and Outliers Rejection

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4th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Bouguila, Nizar Ziou, Djemel Boutemedjet, Sabri Concordia Univ Concordia Inst Informat Syst Engn Montreal PQ H3G 2W1 Canada Univ Sherbrooke Dept dInformatique Sherbrooke PQ J1K 2R1 Canada

ISBN: (纸本)9783642217869

A method for simultaneous non-Gaussian data clustering, feature selection and outliers rejection is proposed in this paper. the proposed approach is based on finite generalized Dirichlet mixture models learned within a framework including expectation-maximization updates for model parameters estimation and minimum message length criterion for model selection. through a challenging application involving texture images discrimination, it is demonstrated that the developed procedure performs effectively in avoiding outliers and selecting relevant features.

关键词： Maximum principle

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Analysing frequent sequential patterns of collaborative learning activity around an interactive tabletop

Analysing frequent sequential patterns of collaborative lear...

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4th international conference on Educational data mining, EDM 2011

作者： Martinez, R. Yacef, K. Kay, J. Al-Qaraghuli, A. Kharrufa, A. School of Information Technologies University of Sydney Australia Faculty of Information Science and Technology UKM Malaysia School of Computing Science Newcastle University United Kingdom

ISBN: (纸本)9789038625379

Electronic traces of activity have the potential to be an invaluable source to understand the strategies followed by groups of learners working collaboratively around a tabletop. However, in tabletop and other co-located learning settings, high amounts of unconstrained actions can be performed by different students simultaneously. this paper introduces a data mining approach that exploits the log traces of a problem-solving tabletop application to extract patterns of activity in order to shed light on the strategies followed by groups of learners. the objective of the data mining task is to discover which frequent sequences of actions differentiate high achieving from low achieving groups. An important challenge is to interpret the raw log traces, taking the user identification into account, and pre-process this data to make it suitable for mining and discovering meaningful patterns of interaction. We explore two methods for mining sequential patterns. We compare these two methods by evaluating the information that they each discover about the strategies followed by the high and low achieving groups. Our key contributions include the design of an approach to find frequent sequential patterns from multiuser co-located settings, the evaluation of the two methods, and the analysis of the results obtained from the sequential pattern mining.

关键词： Problem solving

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Artificial Intelligence Applications and Innovations 2011

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丛书名： IFIP Advances in Information and Communication Technology

2011年

作者： Lazaros S. Iliadis Ilias Maglogiannis Harris Papadopoulos

ISBN: (数字)9783642239601

ISBN: (纸本)9783642239595

the two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international conference on Engineering Applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. the 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. the second volume includes the papers that were accepted for presentation at the AIAI 2011 conference. they are organized in topical sections on computer vision and robotics, classification/pattern recognition, financial and management applications of AI, fuzzy systems, learning and novel algorithms, recurrent and radial basis function ANN, machine learning, generic algorithms, data mining, reinforcement learning, Web applications of ANN, medical applications of ANN and ethics of AI, and environmental and earth applications of AI. the volume also contains the accepted papers from the First Workshop on Computational Intelligence in Software Engineering (CISE 2011) and the Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2011).

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Low rank metric learning with manifold regularization

Low rank metric learning with manifold regularization

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11th IEEE international conference on data mining, ICDM 2011

作者： Zhong, Guoqiang Huang, Kaizhu Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769544083

In this paper, we present a semi-supervised method to learn a low rank Mahalanobis distance function. Based on an approximation to the projection distance from a manifold, we propose a novel parametric manifold regularizer. In contrast to previous approaches that usually exploit side information only, our proposed method can further take advantages of the intrinsic manifold information from data. In addition, we focus on learning a metric of low rank directly;this is different from traditional approaches that often enforce the l1 norm on the metric. the resulting configuration is convex with respect to the manifold structure and the distance function, respectively. We solve it with an alternating optimization algorithm, which proves effective to find a satisfactory solution. For efficient implementation, we even present a fast algorithm, in which the manifold structure and the distance function are learned independently without alternating minimization. Experimental results over 12 standard UCI data sets demonstrate the advantages of our method. © 2011 IEEE.

关键词： Iterative methods

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Fast and robust graph-based transductive learning via minimum tree cut

Fast and robust graph-based transductive learning via minimu...

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11th IEEE international conference on data mining, ICDM 2011

作者： Zhang, Yan-Ming Huang, Kaizhu Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100090 China

ISBN: (纸本)9780769544083

In this paper, we propose an efficient and robust algorithm for graph-based transductive classification. After approximating a graph with a spanning tree, we develop a linear-time algorithm to label the tree such that the cut size of the tree is minimized. this significantly improves typical graphbased methods, which either have a cubic time complexity (for a dense graph) or O(kn 2) (for a sparse graph with k denoting the node degree). Furthermore, our method shows great robustness to the graph construction both theoretically and empirically;this overcomes another big problem of traditional graph-based methods. In addition to its good scalability and robustness, the proposed algorithm demonstrates high accuracy. In particular, on a graph with 400, 000 nodes (in which 10, 000 nodes are labeled) and 10, 455, 545 edges, our algorithm achieves the highest accuracy of 99.6% but takes less than 10 seconds to label all the unlabeled data. © 2011 IEEE.

关键词： Supervised learning

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On Merging the Fields of Neural Networks and Adaptive data Structures to Yield New pattern recognition Methodologies

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4th international conference on pattern recognition and machine Intelligence (PReMI)

作者： Oommen, B. John Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783642217869

the aim of this talk is to explain a pioneering exploratory research endeavour that attempts to merge two completely different fields in Computer Science so as to yield very fascinating results. these are the well-established fields of Neural Networks (NNs) and Adaptive data Structures (ADS) respectively. the field of NNs deals with the training and learning capabilities of a large number of neurons, each possessing minimal computational properties. On the other hand, the field of ADS concerns designing, implementing and analyzing data structures which adaptively change with time so as to optimize some access criteria. In this talk, we shall demonstrate how these fields can be merged, so that the neural elements are themselves linked together using a data structure. this structure can be a singly-linked or doubly-linked list, or even a Binary Search Tree (BST). While the results themselves are quite generic, in particular, we shall, as a prima facie case, present the results in which a Self-Organizing Map (SOM) with an underlying BST structure can be adaptively re-structured using conditional rotations. these rotations on the nodes of the tree are local and are performed in constant time, guaranteeing a decrease in the Weighted Path Length of the entire tree. As a result, the algorithm, referred to as the Tree-based Topology-Oriented SOM with Conditional Rotations (TTO-CONROT), converges in such a manner that the neurons are ultimately placed in the input space so as to represent its stochastic distribution. Besides, the neighborhood properties of the neurons suit the best BST that represents the data.

关键词： Self organizing maps

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