標(biāo)題: Titlebook: Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and ; Proceedings of the 1 Thomas Villmann, [打印本頁(yè)] 作者: controllers 時(shí)間: 2025-3-21 18:31
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 影響因子(影響力)
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 影響因子(影響力)學(xué)科排名
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 被引頻次
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 被引頻次學(xué)科排名
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 年度引用
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 年度引用學(xué)科排名
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 讀者反饋
書(shū)目名稱Advances in Self-Organizing Maps, Learning Vector Quantization, Interpretable Machine Learning, and 讀者反饋學(xué)科排名
作者: Vldl379 時(shí)間: 2025-3-21 20:38 作者: Paradox 時(shí)間: 2025-3-22 02:34
Gruppenarbeit in der industriellen Praxis,gree to which the process of VQ distorts the representation of this density, and the theoretical efficiency of estimators of these densities. In our analysis, . theory from kernel density estimation relates the number of VQ prototypes to observed sample size, dimension, and complexity, all of which intuitively influence codebook sizing.作者: 在駕駛 時(shí)間: 2025-3-22 06:03
Thomas Villmann,Marika Kaden,Frank-Michael Schleif Provides 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: COMA 時(shí)間: 2025-3-22 09:30 作者: Invigorate 時(shí)間: 2025-3-22 15:50 作者: 抵押貸款 時(shí)間: 2025-3-22 18:36 作者: palliate 時(shí)間: 2025-3-22 22:35 作者: 寄生蟲(chóng) 時(shí)間: 2025-3-23 05:12
Dieter Sandner Dipl.-Psych. M. A. to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensi作者: podiatrist 時(shí)間: 2025-3-23 08:57
Dieter Sandner Dipl.-Psych. M. A. min-max-prototypes. These prototypes can be identified with hyperboxes in the data space. Keeping the general GLVQ cost function, we redefine the Hebb-responsibilities for min-max-prototypes and derive consistent learning rules for stochastic gradient descent learning. We demonstrate that the resul作者: Enzyme 時(shí)間: 2025-3-23 12:40 作者: Senescent 時(shí)間: 2025-3-23 15:05
https://doi.org/10.1007/978-3-642-71247-0that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space..SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings..Vector quantization ma作者: PURG 時(shí)間: 2025-3-23 18:12
https://doi.org/10.1007/978-3-642-71247-0 solves this issue by projecting high-dimensional data into a low-dimensional space where meaningful distances allow unbiased function estimation. However, projection pursuit only considers projections onto lines and the unbiased function depends on the sample size. We introduce deep projection purs作者: 使成核 時(shí)間: 2025-3-23 23:18 作者: cumber 時(shí)間: 2025-3-24 05:39
Dorothea Ritter-R?hr (Psychoanalyse)tended to improve clustering of the resulting output space. Improvement is achieved by abandoning the SOM’s rigid lattice structure in favor of a more expressive output topology afforded by Uniform Manifold Approximation and Projection (UMAP), which is incrementally learned in conjunction with SOUMA作者: bromide 時(shí)間: 2025-3-24 10:01
https://doi.org/10.1007/978-3-322-83403-4we consider linear data mappings included in vector quantization models, such as .-means++, neural gas, or self-organizing maps, to achieve representations in a lower-dimensional data space. To this end, we show how additional data knowledge can be integrated into the models. The additional data str作者: 無(wú)所不知 時(shí)間: 2025-3-24 13:38 作者: 混合 時(shí)間: 2025-3-24 16:14
https://doi.org/10.1007/978-3-322-83403-4ss of the approach for the detection and differential diagnosis of Primary Aldosteronism (PA), which has been addressed in a recent retrospective study. Here, we mainly present results for the application of the prototype based Generalized Matrix Relevance Learning Vector Quantization for the classi作者: 嚴(yán)重傷害 時(shí)間: 2025-3-24 23:00
https://doi.org/10.1007/978-3-322-83403-4es include variants of Self Organizing Maps in the unsupervised setting or variants of Learning Vector Quantization in the supervised one). The prototype based formulations make such models intuitive and naturally amenable to interpretation. I will present a series of generalizations and extensions 作者: 含鐵 時(shí)間: 2025-3-25 02:19
Gruppenarbeit in der industriellen Praxis,gree to which the process of VQ distorts the representation of this density, and the theoretical efficiency of estimators of these densities. In our analysis, . theory from kernel density estimation relates the number of VQ prototypes to observed sample size, dimension, and complexity, all of which 作者: Notify 時(shí)間: 2025-3-25 05:44
https://doi.org/10.1007/978-3-322-83403-4pecific use case, it is frequently adequate to compute only a subset of dominant eigenvectors or utilize estimations. Handling this task for large matrices poses a challenge, as standard machine learning packages often lack suitable implementations. We explores various techniques for approximating d作者: 預(yù)兆好 時(shí)間: 2025-3-25 08:36
Gruppenarbeit in der industriellen Praxis,irichlet Allocation) have significant drawbacks, including complex parameter settings. Additionally, these methods often yield low-quality results. Therefore, improving the outcomes of topic modeling is a crucial goal. In this paper, we compare the performance of LDA with a recent topic modeling app作者: myalgia 時(shí)間: 2025-3-25 13:29
https://doi.org/10.1007/978-3-322-83403-4ntify their relevance, either with respect to a local decision or a global model. Feature relevance determination constitutes a foundation for feature selection, and it enables an intuitive insight into the rational of model decisions. Indeed, it constitutes one of the oldest and most prominent expl作者: morale 時(shí)間: 2025-3-25 19:45 作者: 公司 時(shí)間: 2025-3-25 21:14
https://doi.org/10.1007/978-3-322-83403-4 for prototype-based models with the emphasis on interpretability. In this regard, we will show how the learning rules are associated to the underlying decision making of such models. Moreover, the work concludes by giving possible interpretations of these rules and anchor points for developing rela作者: 全能 時(shí)間: 2025-3-26 00:54 作者: byline 時(shí)間: 2025-3-26 04:44
Conference proceedings 2024 (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt-weida), Germany, on July 10–12, 2024..The book highlights new developments in the field of interpretable and explainable machine learning for classification tasks, data compression and visualization. Thereby, the main fo作者: fluffy 時(shí)間: 2025-3-26 10:15
Dieter Sandner Dipl.-Psych. M. A.ervised learning of the Growing Self-Organizing Array (GSOA) modified to address the constrained minimal data retrieving time. The proposed method is compared with a baseline based on a sampling-based decoupled approach, and the results support the feasibility of both proposed solvers in random instances.作者: follicular-unit 時(shí)間: 2025-3-26 13:43
https://doi.org/10.1007/978-3-642-71247-0erpretable models. Finally, we show the ability to maintain group properties in the projection space. Due to these applications, deep projection pursuit is a flexible design paradigm with various use cases.作者: CURT 時(shí)間: 2025-3-26 20:48
Gruppenarbeit in der industriellen Praxis,s. We also highlight the benefits of post-processing the clustering results before modeling topics in the CFMf approach. Our reference dataset consists of 16,917 full-text articles on the philosophy of science.作者: sigmoid-colon 時(shí)間: 2025-3-26 22:14
Gruppenarbeit in der industriellen Praxis,st validate the proposed curves on artificial benchmark data against the ARC as a baseline. We then show on benchmarks and medical, real-world data with class imbalances that the proposed precision- and recall-curves yield more accurate insights into classifier performance than ARCs.作者: 音樂(lè)學(xué)者 時(shí)間: 2025-3-27 02:59 作者: HUMID 時(shí)間: 2025-3-27 07:11 作者: 階層 時(shí)間: 2025-3-27 10:48 作者: Creditee 時(shí)間: 2025-3-27 14:59 作者: Outmoded 時(shí)間: 2025-3-27 20:05
,New Cloth Unto an?Old Garment: SOM for?Regeneration Learning, cross-modal representations in a topologically coherent map. This approach enables bidirectional predictive/regenerative mapping between domains. We evaluate the potential of this method for an unsolved (so far!) practical problem in petroleum geoscience.作者: 赤字 時(shí)間: 2025-3-27 22:14 作者: 口訣 時(shí)間: 2025-3-28 04:36 作者: 沐浴 時(shí)間: 2025-3-28 07:48 作者: freight 時(shí)間: 2025-3-28 12:07 作者: expository 時(shí)間: 2025-3-28 16:50
,Practical Approaches to?Approximate Dominant Eigenvalues in?Large Matrices,ominant eigenvectors in the context of potentially large symmetric, real-valued matrices and offer an overview of established methods, analyzing their potentials and limitations, including implementation details.作者: 發(fā)酵劑 時(shí)間: 2025-3-28 20:23 作者: 概觀 時(shí)間: 2025-3-29 00:50 作者: 領(lǐng)帶 時(shí)間: 2025-3-29 05:02
https://doi.org/10.1007/978-3-322-83403-4ortant to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage.作者: 沒(méi)血色 時(shí)間: 2025-3-29 10:38
https://doi.org/10.1007/978-3-322-83403-4g decision making of such models. Moreover, the work concludes by giving possible interpretations of these rules and anchor points for developing related explanations and designing comprehensible learning rules.作者: 低能兒 時(shí)間: 2025-3-29 13:03 作者: Ingest 時(shí)間: 2025-3-29 18:09
,Exploring Data Distributions in?Machine Learning Models with?SOMs,ortant to adequately select representative datasets. In this work, we combine ML prediction and Self-Organizing Maps-based exploration to build an interpretable machine learning model and to characterize those data that are most difficult to predict in the validation stage.作者: fiscal 時(shí)間: 2025-3-29 21:01
,About Interpretable Learning Rules for?Vector Quantizers - A Methodological Approach,g decision making of such models. Moreover, the work concludes by giving possible interpretations of these rules and anchor points for developing related explanations and designing comprehensible learning rules.作者: surmount 時(shí)間: 2025-3-30 00:54 作者: tattle 時(shí)間: 2025-3-30 06:25 作者: 衰弱的心 時(shí)間: 2025-3-30 10:19
Gruppenarbeit in der industriellen Praxis,els that does not depend on a specific fairness definition, and 2) we derive a fair version of learning vector quantization (LVQ) as a specific instantiation. We compare the resulting algorithm against other algorithms from the literature on theoretical and real-world data showing its practical relevance.作者: 決定性 時(shí)間: 2025-3-30 13:20
,Sparse Clustering with?,-Means - Which Penalties and?for?Which Data?,f these methods, and particularly the differences induced by the choices of the penalty terms. It also illustrates the algorithms and model selection tools made available through a recently implemented R package, vimpclust.作者: confide 時(shí)間: 2025-3-30 16:38
,The Beauty of?Prototype Based Learning,ions of prototype based learning to model spaces and Riemannian manifolds. I will also show how prototype based models can be naturally extended to the setting of ordinal regression and learning with privileged information.作者: Conquest 時(shí)間: 2025-3-30 23:46 作者: ABYSS 時(shí)間: 2025-3-31 01:16 作者: 使習(xí)慣于 時(shí)間: 2025-3-31 05:10 作者: INCH 時(shí)間: 2025-3-31 09:17 作者: 非秘密 時(shí)間: 2025-3-31 15:59 作者: Euphonious 時(shí)間: 2025-3-31 17:54 作者: 遵循的規(guī)范 時(shí)間: 2025-3-31 22:36 作者: 迅速飛過(guò) 時(shí)間: 2025-4-1 02:27
,New Cloth Unto an?Old Garment: SOM for?Regeneration Learning,urrent cross-modal representation and regeneration learning rely on supervised deep learning models, this paper aims to revisit the adequacy of unsupervised models in this field. In this regard, we propose a new unsupervised approach that utilizes the SOM as a heteroassociative memory model to learn作者: Abutment 時(shí)間: 2025-4-1 09:47
,Unsupervised Learning-Based Data Collection Planning with?Dubins Vehicle and?Constrained Data Retri to retrieve required data from the site. The planning task is to find a cost-efficient data collection plan to retrieve data from all the stations. For a fixed-wing aerial vehicle flying with a constant forward velocity, the problem is to determine the shortest feasible path that visits every sensi作者: 強(qiáng)有力 時(shí)間: 2025-4-1 13:07 作者: 姑姑在炫耀 時(shí)間: 2025-4-1 17:00
,Sparse Clustering with?,-Means - Which Penalties and?for?Which Data?,ised learning and particularly in clustering. The presence of uninformative features may bias significantly the results of distance-based methods such as .-means for instance. For tackling this issue, different versions of sparse .-means have been introduced, building on the idea of adding some pena作者: 熔巖 時(shí)間: 2025-4-1 19:50
,Is t-SNE Becoming the?New Self-organizing Map? Similarities and?Differences,that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space..SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings..Vector quantization ma作者: confide 時(shí)間: 2025-4-2 01:25
