標(biāo)題: Titlebook: Classification, Clustering, and Data Mining Applications; Proceedings of the M David Banks,Frederick R. McMorris,Wolfgang Gaul Conference p [打印本頁(yè)] 作者: burgeon 時(shí)間: 2025-3-21 18:53
書目名稱Classification, Clustering, and Data Mining Applications影響因子(影響力)
書目名稱Classification, Clustering, and Data Mining Applications影響因子(影響力)學(xué)科排名
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書目名稱Classification, Clustering, and Data Mining Applications網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Classification, Clustering, and Data Mining Applications讀者反饋
書目名稱Classification, Clustering, and Data Mining Applications讀者反饋學(xué)科排名
作者: FAZE 時(shí)間: 2025-3-21 20:52
978-3-540-22014-5Springer-Verlag Berlin Heidelberg 2004作者: Ganglion 時(shí)間: 2025-3-22 02:09 作者: BRIBE 時(shí)間: 2025-3-22 04:37
https://doi.org/10.1007/978-981-10-8980-0e representation of each cluster simultaneously. In its adaptive version, at each iteration of these algorithms there is a different distance for the comparison of each cluster with its representation. In this paper, we present a dynamic cluster method based on .. distances for quantitative data.作者: 水土 時(shí)間: 2025-3-22 12:10
https://doi.org/10.1007/978-3-030-87710-1sed on calculating the center of gravity. We present in this paper an extension of self-organizing maps to dissimilarity data. This extension allows to apply this algorithm to numerous types of data in a convenient way.作者: 傾聽 時(shí)間: 2025-3-22 15:58 作者: 傾聽 時(shí)間: 2025-3-22 19:59 作者: ARC 時(shí)間: 2025-3-22 23:55
Chinese Culture: The Syntax of the Languagenuous stochastic process. The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed. The approach is compared with other methods via an application to stock-exchange data.作者: 斥責(zé) 時(shí)間: 2025-3-23 03:41
Computer-Mediated Teacher Feedback,Several standardization methods are investigated in conjunction with the .-means algorithm under various conditions. We find that traditional standardization methods (i.e., .-scores) are inferior to alternative standardization methods. Future suggestions concerning the combination of standardization and variable selection are considered.作者: TERRA 時(shí)間: 2025-3-23 06:41
Computer-Mediated Teacher Feedback,The paper proposes a new method to control the level of separation of components using a single parameter. An illustration for the latent class model (mixture of conditionally independent multinomial distributions) is provided. Further extensions to other finite mixture models are discussed.作者: 隨意 時(shí)間: 2025-3-23 11:01
Computer-Mediated Teacher Feedback,It was shown recently that by employing ideas from incremental graph connectivity the asymptotic complexity of sum-of-diameters bipartitioning could be reduced. This article further exploits this idea to develop simpler algorithms and reports on an experimental comparison of all algorithms for this problem.作者: Employee 時(shí)間: 2025-3-23 16:56 作者: 飛鏢 時(shí)間: 2025-3-23 18:12
https://doi.org/10.1007/978-3-030-87710-1In many situations, high dimensional data can be considered as sampled functions. We recall in this paper how to implement a Multi-Layer Perceptron (MLP) on such data by approximating a theoretical MLP on functions thanks to basis expansion. We illustrate the proposed method on a phoneme discrimination problem.作者: 注射器 時(shí)間: 2025-3-24 00:57 作者: 緩解 時(shí)間: 2025-3-24 04:08 作者: Allodynia 時(shí)間: 2025-3-24 06:33 作者: 用手捏 時(shí)間: 2025-3-24 13:50
Priors for Neural NetworksNeural networks are commonly used for classification and regression. The Bayesian approach may be employed, but choosing a prior for the parameters presents challenges. This paper reviews several priors in the literature and introduces Jeffreys priors for neural network models. The effect on the posterior is demonstrated through an example.作者: Aggregate 時(shí)間: 2025-3-24 15:13
Phoneme Discrimination with Functional Multi-Layer PerceptronsIn many situations, high dimensional data can be considered as sampled functions. We recall in this paper how to implement a Multi-Layer Perceptron (MLP) on such data by approximating a theoretical MLP on functions thanks to basis expansion. We illustrate the proposed method on a phoneme discrimination problem.作者: FACT 時(shí)間: 2025-3-24 22:40 作者: LUCY 時(shí)間: 2025-3-25 00:16 作者: Pastry 時(shí)間: 2025-3-25 05:35
Catherine Adams,Terrie Lynn Thompson space. Ultrametric distance is defined from p-adic valuation. It is known that ultrametricity is a natural property of spaces that are sparse. Here we look at the quantification of ultrametricity. We also look at data compression based on a new ultrametric wavelet transform. We conclude with comput作者: aptitude 時(shí)間: 2025-3-25 08:53 作者: 高興去去 時(shí)間: 2025-3-25 11:59 作者: 消極詞匯 時(shí)間: 2025-3-25 16:38 作者: 膠水 時(shí)間: 2025-3-25 22:14
https://doi.org/10.1007/978-981-10-8980-0y with the formation of clusters. The method treats only one variable at each stage; a single variable is chosen to split a cluster into two sub-clusters. Then the sub-clusters are successively split until a stopping criterion is satisfied. The original splitting criterion is the solution of a maxim作者: 石墨 時(shí)間: 2025-3-26 03:28 作者: MIME 時(shí)間: 2025-3-26 05:30 作者: 山崩 時(shí)間: 2025-3-26 10:01
https://doi.org/10.1007/978-3-030-87710-1e CAHCVR algorithm (Classification Ascendante Hiérarchique sous Contrainte de contigu?té et par agrégation des Voisins Réciproques). The results are compared to those obtained with the inertia criterion (Ward) in the context of digital image segmentation. New strategies concerning multiple aggregati作者: insular 時(shí)間: 2025-3-26 15:43
Computer-Mediated Peer Response,extend standard pyramids and their underlying one-to-one correspondence with Robinsonian dissimilarities to spatial pyramids where each cluster is “compatible” with a spatial network given by a kind of tessellation called “m/k-network”. We focus on convex spatial pyramids and we show that they are i作者: NUDGE 時(shí)間: 2025-3-26 19:25 作者: 膠狀 時(shí)間: 2025-3-26 21:42 作者: infatuation 時(shí)間: 2025-3-27 02:09 作者: covert 時(shí)間: 2025-3-27 05:31
A Dynamic Cluster Algorithm Based on , , Distances for Quantitative Datae representation of each cluster simultaneously. In its adaptive version, at each iteration of these algorithms there is a different distance for the comparison of each cluster with its representation. In this paper, we present a dynamic cluster method based on .. distances for quantitative data.作者: Exposition 時(shí)間: 2025-3-27 09:50
A Self-Organizing Map for Dissimilarity Datased on calculating the center of gravity. We present in this paper an extension of self-organizing maps to dissimilarity data. This extension allows to apply this algorithm to numerous types of data in a convenient way.作者: 腐敗 時(shí)間: 2025-3-27 15:52
Relative Projection Pursuit and its Applicationerent from reference data sets predefined by the user. In addition, as an application of the method, we develop a new dimension reduction method: sliced inverse regression with relative projection pursuit.作者: 領(lǐng)導(dǎo)權(quán) 時(shí)間: 2025-3-27 18:13 作者: circuit 時(shí)間: 2025-3-27 23:45
PLS Approach for Clusterwise Linear Regression on Functional Datanuous stochastic process. The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed. The approach is compared with other methods via an application to stock-exchange data.作者: 高談闊論 時(shí)間: 2025-3-28 02:12 作者: 熟練 時(shí)間: 2025-3-28 10:14 作者: 雪白 時(shí)間: 2025-3-28 10:30 作者: 有幫助 時(shí)間: 2025-3-28 15:32
Classification, Clustering, and Data Mining ApplicationsProceedings of the M作者: laxative 時(shí)間: 2025-3-28 22:10
https://doi.org/10.1007/978-981-10-8980-0as shown that the maximum likelihood criterion reduces to minimization of the integrated intensity on the domain containing all of the points. This method of clustering is indexed, divisive and monothetic hierarchical, but its performance can be improved through a gluing-back criterion. That criteri作者: 易怒 時(shí)間: 2025-3-29 01:02 作者: 不在灌木叢中 時(shí)間: 2025-3-29 04:58 作者: ablate 時(shí)間: 2025-3-29 08:08
Catherine Adams,Terrie Lynn Thompsone look at the quantification of ultrametricity. We also look at data compression based on a new ultrametric wavelet transform. We conclude with computational implications of prevalent and perhaps ubiquitous ultrametricity.作者: 聯(lián)合 時(shí)間: 2025-3-29 13:39 作者: Corporeal 時(shí)間: 2025-3-29 17:36 作者: harangue 時(shí)間: 2025-3-29 19:48 作者: 外科醫(yī)生 時(shí)間: 2025-3-30 02:04
Clustering by Vertex Density in a Graphsed on a density function De : X → R which is computed first from D. Then, the number of classes, the classes, and the partitions are established using only this density function and the graph edges, with a computational complexity of o(nδ). Monte Carlo simulations, from random Euclidian distances, validate the method.作者: Constituent 時(shí)間: 2025-3-30 04:55 作者: 鋼盔 時(shí)間: 2025-3-30 11:28
https://doi.org/10.1057/9781137346667se objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithms, tabu search, and k-means. Our results are as good as the best of the above methods.作者: mastopexy 時(shí)間: 2025-3-30 13:18 作者: 完成才會(huì)征服 時(shí)間: 2025-3-30 20:34
Computer-Mediated Peer Response, show that spatial pyramids can converge towards geometrical pyramids. We indicate finally that spatial pyramids can give better results than Kohonen mappings and can produce a geometrical representation of conceptual lattices.作者: insomnia 時(shí)間: 2025-3-30 22:31
Clustering by Ant Colony Optimizationse objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithms, tabu search, and k-means. Our results are as good as the best of the above methods.作者: Essential 時(shí)間: 2025-3-31 03:39 作者: 清醒 時(shí)間: 2025-3-31 06:06
Spatial Pyramidal Clustering Based on a Tessellation show that spatial pyramids can converge towards geometrical pyramids. We indicate finally that spatial pyramids can give better results than Kohonen mappings and can produce a geometrical representation of conceptual lattices.作者: 遵循的規(guī)范 時(shí)間: 2025-3-31 12:04
Thinking Ultrametrically space. Ultrametric distance is defined from p-adic valuation. It is known that ultrametricity is a natural property of spaces that are sparse. Here we look at the quantification of ultrametricity. We also look at data compression based on a new ultrametric wavelet transform. We conclude with comput作者: 脆弱帶來 時(shí)間: 2025-3-31 13:37 作者: 擴(kuò)大 時(shí)間: 2025-3-31 21:09 作者: 分解 時(shí)間: 2025-4-1 01:31
A Dynamic Cluster Algorithm Based on , , Distances for Quantitative Datae representation of each cluster simultaneously. In its adaptive version, at each iteration of these algorithms there is a different distance for the comparison of each cluster with its representation. In this paper, we present a dynamic cluster method based on .. distances for quantitative data.
