標(biāo)題: Titlebook: Advances in Self-Organizing Maps and Learning Vector Quantization; Proceedings of the 1 Thomas Villmann,Frank-Michael Schleif,Mandy Lange C [打印本頁] 作者: 水平 時(shí)間: 2025-3-21 19:54
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書目名稱Advances in Self-Organizing Maps and Learning Vector Quantization被引頻次
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書目名稱Advances in Self-Organizing Maps and Learning Vector Quantization讀者反饋
書目名稱Advances in Self-Organizing Maps and Learning Vector Quantization讀者反饋學(xué)科排名
作者: prosthesis 時(shí)間: 2025-3-21 22:02 作者: APRON 時(shí)間: 2025-3-22 03:36 作者: ALE 時(shí)間: 2025-3-22 06:20
Rejection Strategies for Learning Vector Quantization – A Comparison of Probabilistic and Determinising data in many settings. It is shown that (ii) discriminative models provide a better classification accuracy also when combined with reject strategies based on probabilistic models as compared to generative ones.作者: cancellous-bone 時(shí)間: 2025-3-22 12:14
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?ions in order to outline differences and similarities between them. It discuss the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.作者: CHOKE 時(shí)間: 2025-3-22 12:59 作者: 極少 時(shí)間: 2025-3-22 18:45 作者: monopoly 時(shí)間: 2025-3-22 23:55 作者: 使人入神 時(shí)間: 2025-3-23 03:43 作者: propose 時(shí)間: 2025-3-23 09:30
User Defined Conceptual Modeling Gestures,rate this fact in a few benchmarks. Further, we investigate the behavior of the models if this objective is explicitly formalized in the mathematical costs. This way, a smooth transition of the two partially contradictory objectives, discriminative power versus model representativity, can be obtained.作者: 單調(diào)性 時(shí)間: 2025-3-23 11:16 作者: 斗爭(zhēng) 時(shí)間: 2025-3-23 17:15 作者: antidote 時(shí)間: 2025-3-23 18:06
https://doi.org/10.1007/978-3-319-02964-1ions in order to outline differences and similarities between them. It discuss the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.作者: Lacerate 時(shí)間: 2025-3-23 22:29
Somasundaram Valliappan,Calvin Cheetability of the prototypes is lost. In the present paper, we propose to overcome these two issues by using a bagging approach. The results are illustrated on simulated data sets and compared to alternatives found in the literature.作者: Ischemic-Stroke 時(shí)間: 2025-3-24 04:59
Stuart A. Macgregor,Odile Eisensteinarative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them.作者: 我們的面粉 時(shí)間: 2025-3-24 06:53
Mathew Schwartz,Michael Ehrlichoduced. The powerful framework of relevance learning will be discussed, in which parameterized distance measures are adapted together with the prototypes in the same training process. Recent developments and theoretical insights are discussed and example applications in the bio-medical domain are presented in order to illustrate the concepts.作者: 贊美者 時(shí)間: 2025-3-24 12:48
2194-5357 t applications for data mining and visualization in several .The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first作者: 案發(fā)地點(diǎn) 時(shí)間: 2025-3-24 16:57 作者: labile 時(shí)間: 2025-3-24 19:00
https://doi.org/10.1007/978-3-319-02964-1gnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use of a learning factor, evaluated locally, for each unit. Some results in several examples demonstrate the better performance of MS-SOM compared to SOM.作者: Colonoscopy 時(shí)間: 2025-3-25 02:49
Theoretical Background and Methodology,learning. In this study, SOM using correlation coefficients among nucleotides was proposed, and its performance was examined in the experiments through mapping experiments of the genome sequences of several species and classification experiments using Pareto learning SOMs.作者: 保留 時(shí)間: 2025-3-25 06:21
https://doi.org/10.1007/b114579ese features need to be applied. We use this description after certain pre-processing steps as an input for generalized learning vector quantization (GLVQ) to achieve the classification or labeling of the grid cells. Our approach is evaluated on a standard data set from University of Freiburg, showing very promising results.作者: 肥料 時(shí)間: 2025-3-25 11:15 作者: 善于騙人 時(shí)間: 2025-3-25 14:26
Dynamic Formation of Self-Organizing Maps systems and performed continuously in time. The equations governing competition are shown to be able to reconsider dynamically their decision through a mechanism rendering the current decision unstable, which allows to avoid the use of a global reset signal.作者: 怒目而視 時(shí)間: 2025-3-25 16:33 作者: Supplement 時(shí)間: 2025-3-25 20:28
Visualization and Classification of DNA Sequences Using Pareto Learning Self Organizing Maps Based olearning. In this study, SOM using correlation coefficients among nucleotides was proposed, and its performance was examined in the experiments through mapping experiments of the genome sequences of several species and classification experiments using Pareto learning SOMs.作者: 心胸開闊 時(shí)間: 2025-3-26 02:39 作者: 宴會(huì) 時(shí)間: 2025-3-26 07:54
Anomaly Detection Based on Confidence Intervals Using SOM with an Application to Health Monitoringparticular probability distribution of the data and the detection method is based on the distance of new data to the Kohonen map learned with corrected healthy data. We apply the proposed method to the detection of aircraft engine anomalies.作者: GLUE 時(shí)間: 2025-3-26 11:46
Conference proceedings 2014ciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification..This 10作者: Middle-Ear 時(shí)間: 2025-3-26 13:53
Attention Based Classification Learning in GLVQ and Asymmetric Misclassification Assessmentn medicine. Further we also discuss the weighting of importance for the considered classes in the classification problem. We show that both aspects can be seen as a kind of attention based learning strategy.作者: 放逐 時(shí)間: 2025-3-26 18:38
Generative versus Discriminative Prototype Based Classificationrate this fact in a few benchmarks. Further, we investigate the behavior of the models if this objective is explicitly formalized in the mathematical costs. This way, a smooth transition of the two partially contradictory objectives, discriminative power versus model representativity, can be obtained.作者: labile 時(shí)間: 2025-3-26 22:52
RFSOM – Extending Self-Organizing Feature Maps with Adaptive Metrics to Combine Spatial and Textural of the SOM in the 2.5D point cloud, a more stable behavior of the single neurons in their specific body region, and hence, to a more reliable pose model for further computation. The algorithm was evaluated on different data sets and compared to a Self-Organizing Map trained with the spatial dimensions only using the same data sets.作者: 內(nèi)部 時(shí)間: 2025-3-27 02:37 作者: 畫布 時(shí)間: 2025-3-27 08:17 作者: Keratin 時(shí)間: 2025-3-27 12:03
Conference proceedings 2014ited talks.?Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis and time series analysis. Other chapters present the latest the作者: Indent 時(shí)間: 2025-3-27 14:38 作者: heart-murmur 時(shí)間: 2025-3-27 20:14 作者: Brain-Waves 時(shí)間: 2025-3-27 22:04
https://doi.org/10.1007/978-3-319-02964-1namical system overcomes limitations of the original Self-Organizing Map (SOM) model of Kohonen. Both competition and learning are driven by dynamical systems and performed continuously in time. The equations governing competition are shown to be able to reconsider dynamically their decision through作者: interpose 時(shí)間: 2025-3-28 04:51
https://doi.org/10.1007/978-3-319-02964-1s a second level of organization of neurons. MS-SOM units tend to focus the learning process in data space zones with high values of a user-defined magnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use作者: Induction 時(shí)間: 2025-3-28 07:11
Somasundaram Valliappan,Calvin Chee an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). Also, a side effect of the method is that the interpre作者: Conflict 時(shí)間: 2025-3-28 11:39 作者: CRP743 時(shí)間: 2025-3-28 14:41
Stuart A. Macgregor,Odile Eisensteinr, those based on divergences such as stochastic neighbour embedding (SNE). The big advantage of SNE and its variants is that the neighbor preservation is done by optimizing the similarities in both high- and low-dimensional space. This work presents a brief review of SNE-based methods. Also, a comp作者: nocturia 時(shí)間: 2025-3-28 19:28 作者: obnoxious 時(shí)間: 2025-3-29 01:35 作者: 內(nèi)閣 時(shí)間: 2025-3-29 03:25
https://doi.org/10.1007/978-3-642-18012-5ectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in作者: 大量殺死 時(shí)間: 2025-3-29 09:35 作者: limber 時(shí)間: 2025-3-29 11:29
Mathew Schwartz,Michael Ehrlicharticularly intuitive framework, in which to discuss the basic ideas of distance based classification. A key issue is that of chosing an appropriate distance or similarity measure for the task at hand. Different classes of distance measures, which can be incorporated into the LVQ framework, are intr作者: DENT 時(shí)間: 2025-3-29 19:05
User Defined Conceptual Modeling Gestures,tion ability with an intuitive learning paradigm: models are represented by few characteristic prototypes, the latter often being located at class typical positions in the data space. In this article we investigate inhowfar these expectations are actually met by modern LVQ schemes such as robust sof作者: 專心 時(shí)間: 2025-3-29 21:50 作者: 易于出錯(cuò) 時(shí)間: 2025-3-30 03:35
Erdogan Kiran,Ke Liu,Zeynep Bayraktara correction model which is more accurate than the usual one, since we apply different linear models in each cluster of context. We do not assume any particular probability distribution of the data and the detection method is based on the distance of new data to the Kohonen map learned with correcte作者: 地名表 時(shí)間: 2025-3-30 07:40
https://doi.org/10.1007/978-1-4615-4371-8s, a Self-Organizing Map (SOM) will be computed using a set of features where each feature is weighted by a relevance factor (RFSOM). These factors are computed using the generalized matrix learning vector quantization (GMLVQ) and allow to scale the input dimensions according to their relevance. Wit作者: 使閉塞 時(shí)間: 2025-3-30 09:24
https://doi.org/10.1007/978-1-4615-4371-8eural Gas require the use of distance metrics to measure the similarities between feature vectors as well as class prototypes. While the Euclidean distance is used in many cases, the highly correlated features within the hyperspectral representation and the high dimensionality itself favor the use o作者: 阻塞 時(shí)間: 2025-3-30 15:34
Advances in Self-Organizing Maps and Learning Vector Quantization978-3-319-07695-9Series ISSN 2194-5357 Series E-ISSN 2194-5365 作者: Spina-Bifida 時(shí)間: 2025-3-30 20:26
https://doi.org/10.1007/978-3-642-18012-5ectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in practice.作者: 漂亮 時(shí)間: 2025-3-30 21:08 作者: 啪心兒跳動(dòng) 時(shí)間: 2025-3-31 01:59 作者: 吸引人的花招 時(shí)間: 2025-3-31 05:15 作者: 新字 時(shí)間: 2025-3-31 10:02 作者: GLOOM 時(shí)間: 2025-3-31 14:11
Probabilistic Prototype Classification Using t-normsectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in practice.作者: 吸氣 時(shí)間: 2025-3-31 18:41
