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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 3334 条记录，以下是2901-2910 订阅

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Distance metric learning through optimization of ranking

Distance metric learning through optimization of ranking

引用

17th IEEE international conference on data mining Workshops, ICDM Workshops 2007

作者： Gopal, Kreshna Ioerger, thomas R. Department of Computer Science Texas A and M University

ISBN: (纸本)0769530192

data preprocessing is important in machine learning, data mining, and pattern recognition. In particular, selecting relevant features in high-dimensional data is often necessary to efficiently construct models that accurately describe the data. For example, many lazy learning algorithms (like k-Nearest Neighbor) rely on feature-based distance metrics to compare input patterns for the purpose of classification or retrieval from a database. In previous work, we introduced Slider, a distance metric learning method that optimizes the weights of features in a protein model-building application (where features are used to describe patterns of electron density around protein macromolecules). In this work, we demonstrate the usefulness of Slider as a general method for classification, ranking and retrieval, with results on several benchmark datasets. We also compare it to other well-known feature selection or weighting methods. © 2007 IEEE.

关键词： Classification (of information)

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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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An improved feature extraction technique for high volume time series data

An improved feature extraction technique for high volume tim...

引用

4th IASTED international conference on Signal Processing, pattern recognition and Applications

作者： Anstey, Jonathan S. Peters, Dennis K. Dawson, Chris Mem Univ Newfoundland Elect & Comp Engn St John NF Canada INSTRUMAR Ltd St John Turkey

ISBN: (纸本)9780889866461

the field of time series data mining has seen an explosion of interest in recent years. this interest has flowed over into many applications areas, including fiber manufacturing systems. the volume of time series data generated by a fiber monitoring system can be huge. this limits the applicability of data mining algorithms to this problem domain. A widely used solution is to reduce the data size through feature extraction. Four of the mostly commonly used feature extraction techniques are Fourier transforms, Wavelets, Piecewise Aggregate Approximation, and Piecewise Linear Approximation (PLA). In this paper, we first empirically demonstrate that PLA techniques produce the highest quality features for this problem domain. We then introduce a novel PLA algorithm that is shown to produce higher quality features than any other currently available techniques.

关键词： signal processing segmentation feature extraction manufacturing time series

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Using genetic algorithm for data mining optimization in an image database

Using genetic algorithm for data mining optimization in an i...

引用

4th international conference on Fuzzy Systems and Knowledge Discovery

作者： Gao, Li Dai, Shangping Zheng, Shijue Yan, Guanxiang Huazhong Normal Univ Dept Comp Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China

ISBN: (纸本)0769528740

data mining is rapidly evolving areas of research that are at the intersection of several disciplines, including statistics, databases, pattern recognition, and high-performance and parallel computing. In this paper we propose a novel mining algorithm, called ARMAGA (Association rules mining Algorithm based on a novel Genetic Algorithm), to mine the association rules from an image database, where every image is represented by the ARAMGA representation. We first take advantage of the genetic algorithm designed specifically for discovering association rules. Second we propose the Algorithm Compared to the algorithm in [1], and the ARMAGA algorithm avoids generating impossible candidates, and therefore is more efficient in terms of the execution time.

关键词： Genetic algorithms

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Fractal data compression algorithm for vibration signal in fault diagnosis

Fractal data compression algorithm for vibration signal in f...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Du, Bi-Qiang Tang, Gui-Ji N China Elect Power Univ Dept Mech Engn Baoding Hebei 071003 Peoples R China

ISBN: (纸本)9781424410651

In the paper, the fractal property of rotating machinery vibration signals and the principle of fractal data compression are summarized reviewed. Based on the fractal property, an approach for vibration signal data compression and reconstruction is proposed. In this method, a signal is represented by parameters of affine maps and is reconstructed according to self-similarity represented by the IFS parameters. the total data size of such a representation is far less than the original time domain data size. To demonstrate the effectiveness of this method to resolving the bottleneck in remote transmission of large amount signals and improving the capability of remote equipment fault diagnosis system, the presented method has been applied to some actual vibration signals as well as simulation signals.

关键词： vibration signal fault diagnosis fractal data compression

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A hybrid algorithm based on evolution strategies and instance-based learning, used in two-dimensional fitting of brightness profiles in galaxy images 1

引用

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

作者： Gomez, Juan Carlos Fuentes, Olac INAOE Dept Comp Sci Luis Enrique Erro 1 Puebla 72000 Mexico Univ Texas El Paso Comp Sci Dept El Paso TX 79968 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

the hybridization of optimization techniques can exploit the strengths of different approaches and avoid their weaknesses. In this work we present a hybrid optimization algorithm based on the combination of Evolution Strategies (ES) and Locally Weighted Linear Regression (LWLR). In this hybrid a local algorithm (LWLR) proposes a new solution that is used by a global algorithm (ES) to produce new better solutions. this new hybrid is applied in solving an interesting and difficult problem in astronomy, the two-dimensional fitting of brightness profiles in galaxy images. the use of standardized fitting functions is arguably the most powerful method for measuring the large-scale features (e.g. brightness distribution) and structure of galaxies, specifying parameters that can provide insight into the formation and evolution of galaxies. Here we employ the hybrid algorithm ES+LWLR to find models that describe the bi-dimensional brightness profiles for a set of optical galactic images. Models are created using two functions: de Vaucoleurs and exponential, which produce models that are expressed as sets of concentric generalized ellipses that represent the brightness profiles of the images. the problem can be seen as an optimization problem because we need to minimize the difference between the flux from the model and the flux from the original optical image, following a normalized Euclidean distance. We solved this optimization problem using our hybrid algorithm ES+LWLR. We have obtained results for a set of 100 galaxies, showing that hybrid algorithm is very well suited to solve this problem.

关键词： learning algorithms

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A data-adaptive S-transform

A data-adaptive S-transform

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Man, Wei-Shi Wu, Bang-Yu Gao, Jing-Huai Geng, Yu Xian Jiaotong Univ Inst Microwave & Opt Commun Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)9781424410651

the S-transform is a time frequency analysis technique combining properties of the short-time Fourier and wavelet transforms. It provides frequency-dependent resolution while maintaining g a direct relationship with the Fourier spectrum. However, the frequency resolution of S-transform in high-frequency is unsatisfactory. In this paper, we present a data-adaptive S-transform by optimizing the window width according to the measure of 'concentration'. the proposed method is tested on a set of synthetic signals. the result shows that the proposed algorithm achieves higher resolution and energy concentration than the original S-transform and short-time Fourier transform.

关键词： S-transform concentration measure

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A secure and high capacity data hiding scheme for binary images

A secure and high capacity data hiding scheme for binary ima...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Liang, Hua-Qing Ran, Wen-Bo Niu, Xin-Xin China Univ Petr Dept Elect & Informat Engn Beijing 102249 Peoples R China Beijing Univ Posts & Telecommun Informat Secur Ctr Beijing 100876 Peoples R China

ISBN: (纸本)9781424410651

this paper proposes an effective data hiding scheme for a binary host image for the purpose of access control, authentication and copyright protection of digital media. the binary image is divided into blocks of size 2x2. these blocks are classified as embeddable or non-embeddable blocks according to their characteristic values, and a binary data sequence is embedded in those embeddable blocks by changing their characteristic values. the advantages of the method are low calculation, high embedding capacity, good security and maintaining high qualify of the host image.

关键词： binary image data hiding characteristic value embeddable blocks

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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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An on-line measuring fusion model of lumber moisture content based on data fusion algorithm

An on-line measuring fusion model of lumber moisture content...

引用

5th international conference on Wavelet Analysis and pattern recognition

作者： Li, Jian Sun, Li-Ping Liu, De-Sheng NE Forestry Univ Coll Mech & Elect Harbin 150040 Peoples R China

ISBN: (纸本)9781424410651

Lumber moisture content is a key parameter for regulating and controlling wood drying process. Its precision directly affects the drying quality, cost and drying time. In this paper a fusion model capable of on-line measuring lumber moisture content is presented. Models for predicting lumber moisture content are established using both back-propagation neural networks (BPNN) and dynamical recurrent Mural networks (DRNN). Furthermore, the two models are integrated by arithmetic average and recursive estimation algorithm. the simulation result, which is worked out by experimental data, shows that fusion model have a higher predictive precision than any one of BP Mural network's and DRNN's, therefore, this method is proved to be feasible.

关键词： lumber moisture content data fusion algorithm neural network fusion model

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