標題: Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization; Dedicated to the Mem Jan Faigl,Madalina [打印本頁] 作者: culinary 時間: 2025-3-21 17:39
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization影響因子(影響力)
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization影響因子(影響力)學科排名
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書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization網(wǎng)絡公開度學科排名
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization被引頻次
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization被引頻次學科排名
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization年度引用
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization年度引用學科排名
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization讀者反饋
書目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization讀者反饋學科排名
作者: SLUMP 時間: 2025-3-21 23:27 作者: Curmudgeon 時間: 2025-3-22 01:00 作者: compel 時間: 2025-3-22 05:24 作者: 巨頭 時間: 2025-3-22 12:34
https://doi.org/10.1007/978-1-4419-1123-0of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces .-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.作者: recede 時間: 2025-3-22 13:34 作者: wangle 時間: 2025-3-22 20:38
Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory,Dempster-Shafer-theory, which in the original approach was mentioned to be implicitly realized but not explained deeply. Thus, we redefine the CbC keeping the main aspects of positive and negative reasoning about detected components/features and relate this to the Demspster-Shafer-theory of evidence.作者: bizarre 時間: 2025-3-22 22:55
,Inferring ,-nets of?Finite Sets in?a?RKHS,of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces .-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.作者: 錯誤 時間: 2025-3-23 03:32
,Steps Forward to?Quantum Learning Vector Quantization for?Classification Learning on?a?Theoretical t quantum computing patterns and quantum hardware. For this purpose, we introduce a new computing pattern for prototype updates and possible measurement strategies in the quantum computing regime. Further, we consider numerical errors which are induced by the theoretical model and their impact on the learning process.作者: 高貴領導 時間: 2025-3-23 08:23
Jan Faigl,Madalina Olteanu,Jan DrchalProvides recent research in self-organizing maps, learning vector quantization, clustering, and data visualization.Presents computational aspects and applications for data mining and visualization.Con作者: 一瞥 時間: 2025-3-23 13:00 作者: epinephrine 時間: 2025-3-23 17:39 作者: Emg827 時間: 2025-3-23 21:18 作者: 積習已深 時間: 2025-3-24 01:32 作者: malapropism 時間: 2025-3-24 05:26
Computational Surgery and Dual Trainingthe dynamics of property prices. The exact same apartment typically will not have the same price depending on its location in the city - due to specifics of the neighborhoods and even micro-neighborhoods that are difficult to quantify. Traditional methods rely on the so-called hedonic approaches mod作者: Middle-Ear 時間: 2025-3-24 07:38 作者: capsule 時間: 2025-3-24 13:16
https://doi.org/10.1007/978-1-4419-1123-0of empirical inference: the hierarchical agglomerative clustering and the computation of minimum enclosing balls. It produces .-nets whose cardinalities are smaller than those obtained with state-of-the-art methods.作者: COLON 時間: 2025-3-24 15:23 作者: 有罪 時間: 2025-3-24 21:30 作者: TRACE 時間: 2025-3-25 01:55 作者: 變量 時間: 2025-3-25 04:57
D. Thanoon,M. Garbey,B. L. Bass. However, these are usually scattered across independent libraries, making their integration and comparative application in data exploration difficult. Additionally, few visualizations focus on the dynamics of data forming a time series, while the importance of understanding data evolution is gaini作者: gain631 時間: 2025-3-25 08:14
E. Brian Butler,Paul E. Sovelius,Nancy Huynho their ability to create new possibly meaningful representations of materials from the explored space of examples. In this brief study, we investigate the relative usefulness of different representations of small organic molecules using a variational autoencoder model. Exploratory visualization of 作者: 猛然一拉 時間: 2025-3-25 12:33
Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data VisualizationDedicated to the Mem作者: badinage 時間: 2025-3-25 18:32 作者: aristocracy 時間: 2025-3-25 21:54
,Machine Learning and?Data-Driven Approaches in?Spatial Statistics: A Case Study of?Housing Price Esaphical, economical and infrastructural data in order to bring out the socio-spatial structure of a city and then use this cluster information into the spatial diffusion process of the GWR. SOM gives a notion of proximity between clusters and thus provides a multi-scale degree of similarity (unlike 作者: Ebct207 時間: 2025-3-26 01:22 作者: 不能根除 時間: 2025-3-26 05:39 作者: Cumbersome 時間: 2025-3-26 11:37
Computational Surgery and Dual Trainingnce in the context of a sequential implementation. In the perspective of hardware implementations, we propose here a parallel version of FastBMU, and we analyse its behavior and its performance. Based on the performed analysis, we finally derive principles of a parallel hardware structure that maxim作者: 整頓 時間: 2025-3-26 13:48
Computational Surgery and Dual Trainingaphical, economical and infrastructural data in order to bring out the socio-spatial structure of a city and then use this cluster information into the spatial diffusion process of the GWR. SOM gives a notion of proximity between clusters and thus provides a multi-scale degree of similarity (unlike 作者: Grasping 時間: 2025-3-26 19:00 作者: Bernstein-test 時間: 2025-3-27 00:25
,Sparse Weighted ,-Means for?Groups of?Mixed-Type Variables,le the data may be described by a large number of features, only a minority of them may be actually informative with regard to the structure. Furthermore, redundant features may also bias the clustering, whether one speaks of redundancy in the informative or the uninformative features. The present c作者: Encumber 時間: 2025-3-27 01:39 作者: Sciatica 時間: 2025-3-27 06:47
,Neural Networks for?Spatial Models,that takes into account the spatial dependence. Usual spatial econometric models are based on a neighbourhood matrix whose elements are linked to geographical distances. We propose to use distances between prototypes resulting from a neural classification instead. The results are at least as well as作者: Musculoskeletal 時間: 2025-3-27 11:52 作者: 新字 時間: 2025-3-27 15:50
Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory,Dempster-Shafer-theory, which in the original approach was mentioned to be implicitly realized but not explained deeply. Thus, we redefine the CbC keeping the main aspects of positive and negative reasoning about detected components/features and relate this to the Demspster-Shafer-theory of evidence作者: 異端邪說2 時間: 2025-3-27 21:25 作者: 未完成 時間: 2025-3-27 22:00 作者: 反叛者 時間: 2025-3-28 04:43 作者: peritonitis 時間: 2025-3-28 09:38
,GNG-based Clustering of?Risk-aware Trajectories into?Safe Corridors,pulated environments induces additional risk to people and properties on the ground. Risk-aware planning can mitigate the risk by preferring flying above low-risk areas such as rivers or brownfields. Finding such trajectories is computationally demanding, but they can be precomputed for areas that a作者: CORE 時間: 2025-3-28 11:48 作者: 感情脆弱 時間: 2025-3-28 18:29 作者: dithiolethione 時間: 2025-3-28 19:28 作者: 無節(jié)奏 時間: 2025-3-29 00:34 作者: oblique 時間: 2025-3-29 03:10
,Visual Insights from?the?Latent Space of?Generative Models for?Molecular Design,e the relative usefulness of different representations of small organic molecules using a variational autoencoder model. Exploratory visualization of the latent space of the model is used to increase the interpretability of the results. Visualization is also used to assist the assessment of the reconstruction quality of the model.作者: 愉快嗎 時間: 2025-3-29 11:09 作者: discord 時間: 2025-3-29 15:24 作者: 領先 時間: 2025-3-29 16:31
