標(biāo)題: Titlebook: Advances in Self-Organizing Maps; 7th International Wo José C. Príncipe,Risto Miikkulainen Conference proceedings 2009 Springer-Verlag Berl [打印本頁] 作者: Hallucination 時間: 2025-3-21 18:18
書目名稱Advances in Self-Organizing Maps影響因子(影響力)
書目名稱Advances in Self-Organizing Maps影響因子(影響力)學(xué)科排名
書目名稱Advances in Self-Organizing Maps網(wǎng)絡(luò)公開度
書目名稱Advances in Self-Organizing Maps網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advances in Self-Organizing Maps被引頻次
書目名稱Advances in Self-Organizing Maps被引頻次學(xué)科排名
書目名稱Advances in Self-Organizing Maps年度引用
書目名稱Advances in Self-Organizing Maps年度引用學(xué)科排名
書目名稱Advances in Self-Organizing Maps讀者反饋
書目名稱Advances in Self-Organizing Maps讀者反饋學(xué)科排名
作者: MIME 時間: 2025-3-21 21:51 作者: 地殼 時間: 2025-3-22 01:13 作者: Sedative 時間: 2025-3-22 06:17 作者: 出價 時間: 2025-3-22 11:25 作者: Prostatism 時間: 2025-3-22 15:28
https://doi.org/10.1007/978-3-031-52464-6In this paper, we discuss problems related to the basic Semantic Web methodologies that are based on predicate logic and related formalisms. We discuss complementary and alternative approaches. In particular, we suggest how the Self-Organizing Map can be a basis for making the Semantic Web more semantic.作者: Adrenaline 時間: 2025-3-22 17:40
Concept Mining with Self-Organizing Maps for the Semantic Web,In this paper, we discuss problems related to the basic Semantic Web methodologies that are based on predicate logic and related formalisms. We discuss complementary and alternative approaches. In particular, we suggest how the Self-Organizing Map can be a basis for making the Semantic Web more semantic.作者: 巨碩 時間: 2025-3-23 00:32
Early Recognition of Gesture Patterns Using Sparse Code of Self-Organizing Map,ir beginning parts. Therefore, in the case of gesture recognition, we can get a recognition result of human gestures before the gestures have finished. We realize early recognition by using sparse codes of Self-Organizing Map.作者: CRUE 時間: 2025-3-23 02:46 作者: irreducible 時間: 2025-3-23 05:58
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/149648.jpg作者: 哀求 時間: 2025-3-23 10:58 作者: 外星人 時間: 2025-3-23 17:14
https://doi.org/10.1007/978-94-011-3692-1 recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In作者: 割公牛膨脹 時間: 2025-3-23 19:01 作者: Annotate 時間: 2025-3-23 22:24
Skewness and Kurtosis of Safety Marginin computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons among five competitive neural networks (SOM,作者: CAMEO 時間: 2025-3-24 04:05
Computational Stochastic Mechanicss. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too..The maintenance can be improved if an efficient p作者: 分開如此和諧 時間: 2025-3-24 09:44 作者: Infantry 時間: 2025-3-24 14:44 作者: 矛盾 時間: 2025-3-24 16:32
Computational Stochastic Programmingshort term memory structure called Gamma memory. The proposed model allows increasing depth without losing resolution, by adding more contexts. When using a single stage of the Gamma filter, the Merge SOM model is recovered. The temporal quantization error is used as a performance measure. Simulatio作者: animated 時間: 2025-3-24 22:41 作者: Conspiracy 時間: 2025-3-24 23:58
Springer Optimization and Its Applicationslow-dimensional output space. Most swarm methods are derivatives of the Ant Colony Clustering (ACC) approach proposed by Lumer and Faieta. Compared to clustering on Emergent Self-Organizing Maps (ESOM) these methods usually perform poorly in terms of topographic mapping and cluster formation. A unif作者: 自傳 時間: 2025-3-25 06:50 作者: 懶鬼才會衰弱 時間: 2025-3-25 07:45
M. V. K. Karthik,Pratyoosh ShuklaOM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the topology and minimizing the quantization error. I作者: conservative 時間: 2025-3-25 11:50
https://doi.org/10.1007/978-3-319-02964-1ir beginning parts. Therefore, in the case of gesture recognition, we can get a recognition result of human gestures before the gestures have finished. We realize early recognition by using sparse codes of Self-Organizing Map.作者: temperate 時間: 2025-3-25 18:54 作者: 儲備 時間: 2025-3-25 20:52
https://doi.org/10.1007/978-3-319-02964-1ctors using a finite set of models. In both methods, the quantization error (QE) of an input vector can be expressed, e.g., as the Euclidean norm of the difference of the input vector and the best-matching model. Since the models are usually optimized in the VQ so that the sum of the squared QEs is 作者: Macronutrients 時間: 2025-3-26 01:17 作者: CRATE 時間: 2025-3-26 04:50 作者: DEVIL 時間: 2025-3-26 12:01 作者: 身心疲憊 時間: 2025-3-26 14:24 作者: 衣服 時間: 2025-3-26 17:26
https://doi.org/10.1007/978-3-319-02964-1e self-organisation principle is an alternative research direction to the mainstream research in visual object categorisation and its importance for the ultimate challenge, unsupervised visual object categorisation, needs to be investigated.作者: 錯 時間: 2025-3-27 00:04
https://doi.org/10.1007/978-3-319-02964-1ependent set of test vectors. An explanation seems to ensue from statistics. Each model vector in the VQ is determined as the average of those training vectors that are mapped into the same Voronoi domain as the model vector. On the contrary, each model vector of the SOM is determined as a weighted 作者: SSRIS 時間: 2025-3-27 01:23 作者: Demonstrate 時間: 2025-3-27 06:34 作者: ENDOW 時間: 2025-3-27 10:22
Fault Prediction in Aircraft Engines Using Self-Organizing Maps,ve data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them correspo作者: 人類的發(fā)源 時間: 2025-3-27 15:31
Bag-of-Features Codebook Generation by Self-Organisation,e self-organisation principle is an alternative research direction to the mainstream research in visual object categorisation and its importance for the ultimate challenge, unsupervised visual object categorisation, needs to be investigated.作者: deviate 時間: 2025-3-27 19:15
On the Quantization Error in SOM vs. VQ: A Critical and Systematic Study,ependent set of test vectors. An explanation seems to ensue from statistics. Each model vector in the VQ is determined as the average of those training vectors that are mapped into the same Voronoi domain as the model vector. On the contrary, each model vector of the SOM is determined as a weighted 作者: Hiatus 時間: 2025-3-27 22:57 作者: observatory 時間: 2025-3-28 02:25
Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas, recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In作者: optional 時間: 2025-3-28 08:13 作者: 逗它小傻瓜 時間: 2025-3-28 12:09 作者: analogous 時間: 2025-3-28 15:18
Fault Prediction in Aircraft Engines Using Self-Organizing Maps,s. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too..The maintenance can be improved if an efficient p作者: 技術(shù) 時間: 2025-3-28 20:02
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ,ss. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantization (LVQ)作者: 阻撓 時間: 2025-3-29 00:33
Application of Supervised Pareto Learning Self Organizing Maps and Its Incremental Learning,tors and applied SP-SOM to the biometric authentication system which uses multiple behavior characteristics as feature vectors. In this paper, we examine performance of SP-SOM for the generic classification problem using iris data set. Furthermore, we propose the incremental learning algorithm for S作者: Demonstrate 時間: 2025-3-29 05:47 作者: Entropion 時間: 2025-3-29 08:47 作者: 甜得發(fā)膩 時間: 2025-3-29 14:01 作者: dissolution 時間: 2025-3-29 17:39
Cartograms, Self-Organizing Maps, and Magnification Control,rts with a brief explanation of what a cartogram is, how it can be used, and what sort of metrics can be used to assess its quality. The methodology for creating a cartogram with a SOM is then presented together with an explanation of how the magnification effect can be compensated in this case by p作者: arousal 時間: 2025-3-29 22:14
ViSOM for Dimensionality Reduction in Face Recognition,OM) and growing ViSOM (gViSOM) are two recently proposed variants for a more faithful, metric-based and direct data representation. They learn local quantitative distances of data by regularizing the inter-neuron contraction force while capturing the topology and minimizing the quantization error. I作者: Excise 時間: 2025-3-30 00:57 作者: 寒冷 時間: 2025-3-30 07:09 作者: EXUDE 時間: 2025-3-30 09:01
On the Quantization Error in SOM vs. VQ: A Critical and Systematic Study,ctors using a finite set of models. In both methods, the quantization error (QE) of an input vector can be expressed, e.g., as the Euclidean norm of the difference of the input vector and the best-matching model. Since the models are usually optimized in the VQ so that the sum of the squared QEs is 作者: 切碎 時間: 2025-3-30 14:57 作者: Contracture 時間: 2025-3-30 18:46
Career-Path Analysis Using Optimal Matching and Self-Organizing Maps,ors propose distances well adapted to the data, but are limiting their analysis to hierarchical clustering. Other authors apply sophisticated methods, but only after paying the price of transforming the categorical data into continuous, via a factorial analysis. The latter approach has an important 作者: ATP861 時間: 2025-3-30 20:51
Network-Structured Particle Swarm Optimizer with Various Topology and Its Behaviors,topology as rectangular, hexagonal, cylinder and toroidal. We apply NS-PSO with various topology to optimization problems. We investigate their behaviors and evaluate what kind of topology would be the most appropriate for each function.作者: Ethics 時間: 2025-3-31 03:35 作者: 保守黨 時間: 2025-3-31 08:49 作者: 地殼 時間: 2025-3-31 10:39
Computational Stochastic Programmingsual AP. To evaluate the performance of FAP we compare the clustering results of FAP for different experimental and real world problems with solutions obtained by employing Median Fuzzy c-Means (M-FCM) and Fuzzy c-Means (FCM). As measure for cluster agreements we use a fuzzy extension of Cohen’s . based on t-norms.作者: corporate 時間: 2025-3-31 16:18 作者: dyspareunia 時間: 2025-3-31 19:25 作者: TIA742 時間: 2025-3-31 22:16
