標(biāo)題: Titlebook: Advances in Self-Organizing Maps; 8th International Wo Jorma Laaksonen,Timo Honkela Conference proceedings 2011 Springer-Verlag GmbH Berlin [打印本頁(yè)] 作者: CULT 時(shí)間: 2025-3-21 18:31
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作者: 罐里有戒指 時(shí)間: 2025-3-21 20:57
Contextually Self-Organized Maps of Chinese Wordse SOM, in this work histograms of various word classes or otherwise defined subsets of words were formed on the SOM array. It was found that the words are not only clustered according to the word classes, but joint or overlapping clusters of words from different classes can also be formed according 作者: 暗語(yǔ) 時(shí)間: 2025-3-22 02:05
Assessing the Efficiency of Health Care Providers: A SOM Perspectiveused as input the data from the balance sheets of 300 health care providers, as resulting from the Italian Statistics Institute (ISTAT) database, and we examined their representation obtained both by running classical SOM algorithm, and by modifying it through the replacement of standard Euclidean d作者: 笨拙的我 時(shí)間: 2025-3-22 08:14
Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Seriess a stand-alone clustering technique, its output has also been used as input for second-stage clustering. However, one ambiguity with the SOM clustering is that the degree of membership in a particular cluster is not always easy to judge. To this end, we propose a fuzzy C-means clustering of the uni作者: resuscitation 時(shí)間: 2025-3-22 10:41 作者: 記成螞蟻 時(shí)間: 2025-3-22 15:47
EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualizationts of two consecutive trainings of the self-organizing map. In the first phase, a SOM is trained using every available variable, but only those which characterize the environment are used to compute the winner unit. Therefore, this phase produces an accurate model of the environment. In the second p作者: fidelity 時(shí)間: 2025-3-22 17:31 作者: Cardioversion 時(shí)間: 2025-3-22 23:07
Sparse Functional Relevance Learning in Generalized Learning Vector Quantization set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.作者: 愉快么 時(shí)間: 2025-3-23 02:55 作者: diskitis 時(shí)間: 2025-3-23 06:19
Requirements for the Learning of Multiple Dynamicsing Algorithm of Multiple Dynamics (LAMD), is expected to satisfy the following four requirements. (i) Given a set of time-series sequences for training, estimate the dynamics and their latent variables. (ii) Order the dynamical systems according to the similarities between them. (iii) Interpolate i作者: Mediocre 時(shí)間: 2025-3-23 12:40
Growing Graph Network Based on an Online Gaussian Mixture Modelussian kernels and finding topologies between kernels using graph paths. The proposed method has the following advantages compared with conventional growing neural networks: no permanent increase in nodes (Gaussian kernels), robustness to noise, and increased speed of constructing networks. This pap作者: lavish 時(shí)間: 2025-3-23 15:36 作者: MIR 時(shí)間: 2025-3-23 21:02 作者: Coeval 時(shí)間: 2025-3-23 22:28 作者: ARBOR 時(shí)間: 2025-3-24 03:01
Gamma-Filter Self-Organizing Neural Networks for Time Series Analysiscriptor based on a short term memory structure called Gamma memory. When using a single stage of the Gamma filter, the Merge GNG model is recovered. The .-GNG model is compared to .-Neural Gas, .-SOM, and Merge Neural Gas, using the temporal quantization error as a performance measure. Simulation re作者: 波動(dòng) 時(shí)間: 2025-3-24 09:01 作者: backdrop 時(shí)間: 2025-3-24 14:00 作者: Carcinogen 時(shí)間: 2025-3-24 15:35
A Discussion on Visual Interactive Data Exploration Using Self-Organizing Mapst users in data exploration tasks, a series of software tools emerged which integrate various visualizations. However, the focus of most research was the development of visualizations which improve the support in cluster identification. In order to provide real insight into the data set it is crucia作者: Simulate 時(shí)間: 2025-3-24 19:39 作者: 堅(jiān)毅 時(shí)間: 2025-3-25 02:03
Assessing the Efficiency of Health Care Providers: A SOM Perspective providers is completely new; (b) SOM graph mining shows, in turn, elements of innovations for the way the adjacency matrix is formed, with the connections among SOM winner nodes used as starting point to the process.作者: CHOP 時(shí)間: 2025-3-25 06:48 作者: Missile 時(shí)間: 2025-3-25 09:26 作者: 廚房里面 時(shí)間: 2025-3-25 13:47 作者: 進(jìn)入 時(shí)間: 2025-3-25 19:22 作者: 提名 時(shí)間: 2025-3-25 21:17 作者: 斑駁 時(shí)間: 2025-3-26 03:58 作者: HALO 時(shí)間: 2025-3-26 08:07 作者: SEMI 時(shí)間: 2025-3-26 09:11 作者: 裝勇敢地做 時(shí)間: 2025-3-26 13:48
Topographic Measure Based on External Criteria for Self-Organizing Mapihood function to the pairwise-based measures. Our method can extend any clustering measure based on set or pairwise of data points. The present paper examined the topographic component of the extended measure and revealed an appropriate neighborhood radius of the topographic measures.作者: 深淵 時(shí)間: 2025-3-26 17:16 作者: 量被毀壞 時(shí)間: 2025-3-26 22:47
A Discussion on Visual Interactive Data Exploration Using Self-Organizing Mapsl that users have the possibility of interactively investigating the data set. This work provides an overview of state-of-the-art software tools for SOM-based visual data exploration. We discuss the functionality of software for specialized data sets, as well as for arbitrary data sets with a focus on interactive data exploration.作者: Crohns-disease 時(shí)間: 2025-3-27 03:48 作者: 確定方向 時(shí)間: 2025-3-27 07:48
Atsuhiro Hayashi,Tomoyuki Tarumiy crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.作者: 微枝末節(jié) 時(shí)間: 2025-3-27 12:52
Atsuhiro Hayashi,Tomoyuki Tarumi from the media. Here individuals are represented in two spaces: a static geographical location, and a dynamic political position. The modification of the later leads to a pattern in which both spaces are correlated.作者: 蝕刻 時(shí)間: 2025-3-27 15:05
0302-9743 rnational Workshop on Self-Organizing Maps, WSOM 2011, held in Espoo, Finland, in June 2011. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on plenaries; financial and societal applications; theory and作者: 雄辯 時(shí)間: 2025-3-27 18:02 作者: 莎草 時(shí)間: 2025-3-27 23:35 作者: backdrop 時(shí)間: 2025-3-28 04:28 作者: Ledger 時(shí)間: 2025-3-28 09:14
Robert Mines,Kai-Yuan Chen,Xiling Shenhe .-GNG model is compared to .-Neural Gas, .-SOM, and Merge Neural Gas, using the temporal quantization error as a performance measure. Simulation results on two data sets are presented: Mackey-Glass time series, and Bicup 2006 challenge time series.作者: 衰老 時(shí)間: 2025-3-28 11:32 作者: 1分開(kāi) 時(shí)間: 2025-3-28 18:30
Growing Graph Network Based on an Online Gaussian Mixture Modelrowing neural networks: no permanent increase in nodes (Gaussian kernels), robustness to noise, and increased speed of constructing networks. This paper presents the theory and algorithm for the proposed method and the results of verification experiments using artificial data.作者: 星星 時(shí)間: 2025-3-28 22:02
Influence of Learning Rates and Neighboring Functions on Self-Organizing Mapsr, inverse-of-time, power series, and heuristics) have been analyzed here. The learning rate has been changed according to epochs and iterations. A comparative analysis has been made with three data sets: glass, wine, and zoo. The quantization error has been measured in order to estimate the SOM quality.作者: squander 時(shí)間: 2025-3-29 00:45 作者: NORM 時(shí)間: 2025-3-29 03:22 作者: Licentious 時(shí)間: 2025-3-29 09:03 作者: 注視 時(shí)間: 2025-3-29 14:22 作者: 蹣跚 時(shí)間: 2025-3-29 16:31 作者: 一夫一妻制 時(shí)間: 2025-3-29 23:13 作者: 惰性女人 時(shí)間: 2025-3-30 03:10
Mining the City Data: Making Sense of Cities with Self-Organizing MapsAlso, it is important to observe how neighbors geographically close are distributed in terms of the mentioned variables. Self-organizing maps are a tool suitable for planners to seek for those correlations, as we show in our results.作者: abduction 時(shí)間: 2025-3-30 06:39
Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Seriesy crises. It allows each time-series point to have a partial membership in all identified, but overlapping, clusters, where the cluster centers express the representative financial states for the companies and countries, while the fluctuations of the membership degrees represent their variations over time.作者: 性冷淡 時(shí)間: 2025-3-30 10:33 作者: 消極詞匯 時(shí)間: 2025-3-30 14:15 作者: Aerate 時(shí)間: 2025-3-30 18:58
Pekka J. Korhonen,Aapo Siljam?kirameters of a local affine transformation associated with each neuron are updated by an evolutionary algorithm and used to map each template’s keypoint in the previous frame to the current one. Computer simulations indicate that the proposed approach presents better results than those obtained by a direct method approach.作者: 詞匯表 時(shí)間: 2025-3-30 22:11
Mahmoud Seyedsadr,Paul L. Corneliusihood function to the pairwise-based measures. Our method can extend any clustering measure based on set or pairwise of data points. The present paper examined the topographic component of the extended measure and revealed an appropriate neighborhood radius of the topographic measures.作者: Adherent 時(shí)間: 2025-3-31 03:40 作者: CROW 時(shí)間: 2025-3-31 08:32
Fiona K. Hamey,Berthold G?ttgensl that users have the possibility of interactively investigating the data set. This work provides an overview of state-of-the-art software tools for SOM-based visual data exploration. We discuss the functionality of software for specialized data sets, as well as for arbitrary data sets with a focus on interactive data exploration.作者: Melodrama 時(shí)間: 2025-3-31 09:58
Sparse Functional Relevance Learning in Generalized Learning Vector Quantization set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.作者: 清真寺 時(shí)間: 2025-3-31 13:54
Relevance Learning in Unsupervised Vector Quantization Based on Divergencesen vector quantization cost function. We consider several widely used models including the neural gas algorithm, the Heskes variant of self-organizing maps and the fuzzy c-means. We apply the relevance learning scheme for divergence based similarity measures between prototypes and data vectors in the vector quantization schemes.作者: Libido 時(shí)間: 2025-3-31 20:19
https://doi.org/10.1007/978-3-642-21566-7ANN; SOM algorithms; bioinspired computing; computational intelligence; natural computing; neural network作者: deactivate 時(shí)間: 2025-3-31 22:22 作者: Dislocation 時(shí)間: 2025-4-1 03:24
Mathematical and Statistical Preliminariesnherently non-Euclidean and modern data formats are connected to dedicated non-Euclidean dissimilarity measures for which classical topographic mapping cannot be used. We give an overview about extensions of topographic mapping to general dissimilarities by means of median or relational extensions. 作者: expansive 時(shí)間: 2025-4-1 06:22
Atsuhiro Hayashi,Tomoyuki Tarumie SOM, in this work histograms of various word classes or otherwise defined subsets of words were formed on the SOM array. It was found that the words are not only clustered according to the word classes, but joint or overlapping clusters of words from different classes can also be formed according 作者: Tortuous 時(shí)間: 2025-4-1 12:55
