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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是71-80 订阅

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

引用

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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data clustering: User's dilemma 1

引用

5th international conference on machine learning and data mining in pattern recognition, mldm 2007

作者： Jain, Anil K. Department of Computer Science and Engineering Michigan State University United States

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

data clustering is a long standing research problem in pattern recognition, computer vision, machine learning, and data mining with applications in a number of diverse disciplines. the goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge about cluster shapes. While a large number of clustering algorithms exist, there is no optimal algorithm. Each clustering algorithm imposes a specific structure on the data and has its own approach for estimating the number of clusters. No single algorithm can adequately handle various cluster shapes and structures that are encountered in practice. Instead of spending our effort in devising yet another clustering algorithm, there is a need to build upon the existing published techniques. In this talk we will address the following problems: (i) clustering via evidence accumulation, (ii) simultaneous clustering and dimensionality reduction, (iii) clustering under pair-wise constraints, and (iv) clustering with relevance feedback. Experimental results show that these approaches are promising in identifying arbitrary shaped clusters in multidimensional data. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Clustering algorithms

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Distributed and shared memory algorithm for parallel mining of association rules 1

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5th international conference on machine learning and data mining in pattern recognition, mldm 2007

作者： Palancar, J. Hernández Tormo, O. Fraxedas Cárdenas, J. Festán Leán, R. Hernández 7a # 21812 e/ 218 y 222 Rpto. Siboney Playa La Habana Cuba

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

the search for frequent patterns in transactional databases is considered one of the most important data mining problems. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. Almost all of these algorithms make repeated passes over the dataset to determine the set of frequent itemsets, thus implying high I/O overhead. In the parallel case, most algorithms perform a sum-reduction at the end of each pass to construct the global counts, also implying high synchronization cost. We present a novel algorithm that exploits efficiently the trade-offs between computation, communication, memory usage and synchronization. the algorithm was implemented over a cluster of SMP nodes combining distributed and shared memory paradigms. this paper presents the results of our algorithm on different data sizes experimented on different numbers of processors, and studies the effect of these variations on the overall performance. © Springer-Verlag Berlin Heidelberg 2007.

关键词： data mining

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Dynamic distance-based active learning with SVM 1

引用

5th international conference on machine learning and data mining in pattern recognition, mldm 2007

作者： Jiang, Jun Ip, Horace H. S. Image Computing Group Department of Computer Science Hong Kong City University of Hong Kong Hong Kong Hong Kong

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper, we present a novel active learning strategy, named dynamic active learning with SVM to improve the effectiveness of learning sample selection in active learning. the algorithm is divided into two steps. the first step is similar to the standard distance-based active learning with SVM [1] in which the sample nearest to the decision boundary is chosen to induce a hyperplane that can halve the current version space. In order to improve upon the learning efficiency and convergent rates, we propose in the second step, a dynamic sample selection strategy that operates within the neighborhood of the "standard" sample. theoretical analysis is given to show that our algorithm will converge faster than the standard distance-based technique and using less number of samples while maintaining the same classification precision rate. We also demonstrate the feasibility of the dynamic selection strategy approach through conducting experiments on several benchmark datasets. © Springer-Verlag Berlin Heidelberg 2007.

关键词： learning algorithms

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Advances in machine learning and Cybernetics - 4th international conference, ICMLC 2005, Revised Selected Papers

Advances in Machine Learning and Cybernetics - 4th Internati...

引用

4th international conference on machine learning and Cybernetics, ICMLC 2005

ISBN: (纸本)3540335846

the proceedings contain 115 papers. the topics discussed include: strategy coordination approach for safe learning about novel filtering strategies in multi agent framework;modeling and design of agent based open decision support systems;particle filter method for a centralized multisensor system;design and analysis of a novel load-balancing model based on mobile agent;construction and simulation of the movable propeller turbine neural network model;research and application of data mining in power plant process control and optimization;an efficient algorithm for incremental mining of sequential patterns;repeating pattern discovery from audio stream;a method to eliminate incompatible knowledge and equivalence knowledge;a similarity-aware multiagent-based web content management;evolutionary multi-objective optimization algorithm with preference for mechanical design;and refinement of fuzzy production rules by using a fuzzy-neural approach.

关键词： learning systems

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the angle difference based state transition modeling for the signal pattern classification of the sensor array

The angle difference based state transition modeling for the...

引用

3rd international conference on Cybernetics and Information Technologies, Systems and Applications/4th international conference on Computing, Communications and Control Technologies

作者： Lee, SeungJae Kim, Changhwa Kim, Sangkyung Kim, Aram Kangnung Natl Univ Dept Comp Engn Gangnung Si 210702 Gangwon Do South Korea

ISBN: (纸本)9789806560840

Sensors have been used with various purposes in the human life. A sensor which can be functioned as a part of a signal process unit or a mechanical machine is defined as "a part of a measuring instrument which detects and responds immediately changes of a environment". As a sensor just reports the voltage level respect to detected physical or chemical quantity, it is needed to convert properly into meaning data. In most of cases, a sensor array, which consists of various kinds of sensors are used to detect a environment. there are two classes of methods to analyze signal patterns from a sensor array;the statistical method and the neural network method. One method has weak points comparing with another. One of each method's weak points is that most of statistical methods cannot consider shape characteristics of the signal pattern and neural network methods take too long time in the learning process. In spite of this weakness, the neural network process has been used in most of gas pattern recognition in recent studies. In this paper, we introduce a statistical method using state transition model for gas recognition. this paper focuses on making the accurate state transition model. We call this state transition model as ADSTM(Angle Difference based State Transition Model). through various experiments, we analyze the proposed ADSTM modeling method. the results of experiments show that ADSTM is a fast and reliable statistical method for recognizing a signal pattern of the sensor array.

关键词： gas recognition gas sensor sensor array signal pattern state transition model

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Understanding patterns with different subspace classification

引用

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

作者： Szepannek, G Luebke, K Weihs, C Univ Dortmund Dept Stat Dortmund Germany

ISBN: (纸本)3540269231

By identifying characteristic regions in which classes are dense and also relevant for discrimination a new, intuitive classification method is set up. this method enables a visualized result so the user is provided with an insight into the data with respect to discrimination for an easy interpretation. Additionally, it outperforms Decision trees in a lot of situations and is robust against outliers and missing values.

关键词： pattern recognition

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pattern mining across domain-specific text collections

引用

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

作者： Gillam, Lee Ahmad, Khurshid Department of Computing School of Electronics and Physical Sciences University of Surrey Guildford GU2 7XH United Kingdom

ISBN: (纸本)3540269231

this paper discusses a consistency in patterns of language use across domain-specific collections of text. We present a method for the automatic identification of domain-specific keywords - specialist terms - based on comparing language use in scientific domain-specific text collections with language use in texts intended for a more general audience. the method supports automatic production of collocational networks, and of networks of concepts thesauri, or so-called ontologies. the method involves a novel combination of existing metrics from work in computational linguistics, which can enable extraction, or learning, of these kinds of networks. Creation of ontologies or thesauri is informed by international (ISO) standards in terminology science, and the resulting resource can be used to support a variety of work, including data-mining applications. © Springer-Verlag Berlin Heidelberg 2005.

关键词： data mining

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Disjunctive sequential patterns on single data sequence and its anti-monotonicity

引用

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

作者： Shimizu, K Miura, T HOSEI Univ Dept Elect & Elect Engn Koganei Tokyo 1848584 Japan

In this work, we proposes a novel method for mining frequent disjunctive patterns on single data sequence. For this purpose, we introduce a sophisticated measure that satisfies anti-monotonicity, by which we can discu... 详细信息

ISBN: (纸本)3540269231

关键词： disjunctive sequence pattern anti-monotonicity single data sequence

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Activity and motion detection based on measuring texture change

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

作者： Latecki, Longin Jan Miezianko, Roland Pokrajac, Dragoljub Temple University CIS Dept. Philadelphia PA 19122 United States Delaware State University CIS Dept. Dover DE 19901 United States

ISBN: (纸本)3540269231

We estimate the speed of texture change by measuring the spread of texture vectors in their feature space. this method allows us to robustly detect even very slow moving objects. By learning a normal amount of texture change over time, we are also able to detect increased activities in videos. We illustrate the performance of the proposed techniques on videos from PETS repository and the Temple University Police department. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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