標(biāo)題: Titlebook: Data Science, Learning by Latent Structures, and Knowledge Discovery; Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B? Conference procee [打印本頁] 作者: 古生物學(xué) 時(shí)間: 2025-3-21 17:48
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery影響因子(影響力)
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery影響因子(影響力)學(xué)科排名
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書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery被引頻次
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery被引頻次學(xué)科排名
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書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery年度引用學(xué)科排名
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery讀者反饋
書目名稱Data Science, Learning by Latent Structures, and Knowledge Discovery讀者反饋學(xué)科排名
作者: 最高點(diǎn) 時(shí)間: 2025-3-21 20:41 作者: somnambulism 時(shí)間: 2025-3-22 04:10
Jesica de Armas,Helena Ramalhinho,Stefan Vo? similarity/dissimilarity measures: a similarity measure between concepts (elements) of a lattice and a dissimilarity measure between concept lattices defined on the same set of objects and attributes. Both measures are based on the overhanging relation previously introduced by the author, which are a cryptomorphism of lattices.作者: CLAN 時(shí)間: 2025-3-22 05:46
Berthold Lausen,Sabine Krolak-Schwerdt,Matthias B?Covers theory, methods and applications of data analysis.Inspires for further research in the fields of Data Analysis, Learning by Latent Structures and Knowledge Discovery.Combines the intensive work作者: 元音 時(shí)間: 2025-3-22 09:10 作者: 過渡時(shí)期 時(shí)間: 2025-3-22 15:13 作者: 過渡時(shí)期 時(shí)間: 2025-3-22 19:56 作者: 不能根除 時(shí)間: 2025-3-22 21:57 作者: 鴿子 時(shí)間: 2025-3-23 03:59 作者: 比目魚 時(shí)間: 2025-3-23 06:35
Arturo Pérez Rivera,Martijn Mesta. The prospective clusters can readily be distinguished from background noise and from other forms of outliers. A confirmatory Forward Search, involving control on the sizes of statistical tests, establishes precise cluster membership. The method performs as well as robust methods such as TCLUST. 作者: THE 時(shí)間: 2025-3-23 13:34 作者: 1分開 時(shí)間: 2025-3-23 14:44
https://doi.org/10.1007/978-3-030-31140-7. It permits to define local inertia and local autocorrelation relatively to arbitrary networks. In particular, free partitioned exchanges amount in defining a categorical variable (hard membership), together with canonical spectral scores, identical to Fisher’s discriminant functions. One demonstra作者: Incorruptible 時(shí)間: 2025-3-23 18:33 作者: 遠(yuǎn)足 時(shí)間: 2025-3-23 22:47
Ferry Service Network Design for Kiel fjord which have a common neighbor. In theoretical random graph models, this tendency is described by the clustering coefficient being bounded away from zero. Complex networks also have power-law degree distributions and short average distances (small world phenomena). These are desirable features of ran作者: single 時(shí)間: 2025-3-24 04:18
Jesica de Armas,Helena Ramalhinho,Stefan Vo? similarity/dissimilarity measures: a similarity measure between concepts (elements) of a lattice and a dissimilarity measure between concept lattices defined on the same set of objects and attributes. Both measures are based on the overhanging relation previously introduced by the author, which are作者: hair-bulb 時(shí)間: 2025-3-24 10:27 作者: ECG769 時(shí)間: 2025-3-24 12:16 作者: Detoxification 時(shí)間: 2025-3-24 17:33 作者: 兒童 時(shí)間: 2025-3-24 20:53 作者: integrated 時(shí)間: 2025-3-25 02:47 作者: minaret 時(shí)間: 2025-3-25 07:18
Srikanta Patnaik,Kayhan Tajeddini,Vipul Jain of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classifica作者: 自然環(huán)境 時(shí)間: 2025-3-25 08:17
Somayya Madakam,Rajeev K. Revulagaddatic algorithms for topic tracking often extract general tendencies at a high granularity level and do not provide added value to experts who are looking for more subtle information. In this paper, we focus on the visualization of the co-evolution of terms in tweets in order to facilitate the analysi作者: lobster 時(shí)間: 2025-3-25 13:24 作者: choroid 時(shí)間: 2025-3-25 19:52
https://doi.org/10.1007/978-3-662-44983-7Classification; Data Analysis; Data Science; Data Stream; Knowledge Organization; Latent Structures作者: Outmoded 時(shí)間: 2025-3-25 20:17
978-3-662-44982-0Springer-Verlag Berlin Heidelberg 2015作者: 金盤是高原 時(shí)間: 2025-3-26 03:01
1431-8814 ructures and Knowledge Discovery.Combines the intensive work.This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering and patter作者: Intervention 時(shí)間: 2025-3-26 07:42
Arturo Pérez Rivera,Martijn Mesving control on the sizes of statistical tests, establishes precise cluster membership. The method performs as well as robust methods such as TCLUST. However, it does not require prior specification of the number of clusters, nor of the level of trimming of outliers. In this way it is “user friendly”.作者: 整潔漂亮 時(shí)間: 2025-3-26 09:36 作者: tenuous 時(shí)間: 2025-3-26 16:09
Eduardo Lalla-Ruiz,Martijn Mes,Stefan Vo? networks have power law degree distribution and small diameter (small world phenomena), thus these are desirable features of random graphs used for modeling real life networks. We survey various variants of random intersection graph models, which are important for networks modeling.作者: Breach 時(shí)間: 2025-3-26 19:44 作者: 流逝 時(shí)間: 2025-3-26 21:31
Srikanta Patnaik,Kayhan Tajeddini,Vipul Jainexamples. Experimentations highlight important performance differences for four complementary evaluation measures (Log-Loss, Ranking-Loss, Learning and Prediction Times). The best results are obtained for Multi-label . Nearest Neighbors (ML-.NN), ensemble of classifier chains (ECC), and ensemble of binary relevance (EBR).作者: 胎兒 時(shí)間: 2025-3-27 03:16 作者: otic-capsule 時(shí)間: 2025-3-27 07:51 作者: Amylase 時(shí)間: 2025-3-27 11:18
Finding the Number of Disparate Clusters with Background Contaminationving control on the sizes of statistical tests, establishes precise cluster membership. The method performs as well as robust methods such as TCLUST. However, it does not require prior specification of the number of clusters, nor of the level of trimming of outliers. In this way it is “user friendly”.作者: syring 時(shí)間: 2025-3-27 16:09 作者: 山間窄路 時(shí)間: 2025-3-27 18:25
Recent Progress in Complex Network Analysis: Models of Random Intersection Graphs networks have power law degree distribution and small diameter (small world phenomena), thus these are desirable features of random graphs used for modeling real life networks. We survey various variants of random intersection graph models, which are important for networks modeling.作者: GULLY 時(shí)間: 2025-3-27 22:22 作者: prosthesis 時(shí)間: 2025-3-28 03:55
Letícia Caldas,Rafael Martinelli,Bruno Rosair power indices and multidimensional scaling properties. In particular, formal and numerical studies demonstrate the existence of critical temperatures, where flow-based dissimilarities cease to be squared Euclidean. The clustering potential of medium range temperatures is emphasized.作者: corpuscle 時(shí)間: 2025-3-28 09:12 作者: savage 時(shí)間: 2025-3-28 12:26
Recent Progress in Complex Network Analysis: Properties of Random Intersection Graphsdom graphs used for modeling real life networks. We survey recent results concerning various random intersection graph models showing that they have tunable clustering coefficient, a rich class of degree distributions including power-laws, and short average distances.作者: 變白 時(shí)間: 2025-3-28 16:14 作者: bacteria 時(shí)間: 2025-3-28 22:13
Lecture Notes in Computer Scienceation to a real benchmark credit approval mixed-data set to classify the customers into good/bad classes for credit approval. Our results show the excellent performance of HRBF-NN method in supervised classification tasks.作者: scoliosis 時(shí)間: 2025-3-29 00:05 作者: COWER 時(shí)間: 2025-3-29 06:55
A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model ation to a real benchmark credit approval mixed-data set to classify the customers into good/bad classes for credit approval. Our results show the excellent performance of HRBF-NN method in supervised classification tasks.作者: obsession 時(shí)間: 2025-3-29 09:43
Visual Analysis of Topics in Twitter Based on Co-evolution of Termsation of their co-evolution. An experiment was conducted on real-life datasets in collaboration with an expert in customer relationship management working at the French energy company EDF. The first results show three different kinds of co-evolution of terms: bursty features, reoccurring terms and long periods of activity.作者: Alpha-Cells 時(shí)間: 2025-3-29 12:59
Modernising Official Statistics: A Complex Challenge These organisations come together as partners in the European Statistical System (ESS). This paper describes the ESS, the challenges it faces and the modernisation efforts that have been undertaken based on a redesigned ESS enterprise architecture. It also outlines the probable future direction of 作者: contradict 時(shí)間: 2025-3-29 17:37 作者: phase-2-enzyme 時(shí)間: 2025-3-29 22:21 作者: Recessive 時(shí)間: 2025-3-30 01:37
Clustering of Solar Irradianceefinitively a solution to reduce the storage capacities and, as a result, authorizes to increase the penetration of the photovoltaic units on the power grid. We present the first results of an interdisciplinary research project which involves researchers in energy, meteorology, and data mining, addr作者: 鐵塔等 時(shí)間: 2025-3-30 07:36
Factor Analysis of Local Formalism. It permits to define local inertia and local autocorrelation relatively to arbitrary networks. In particular, free partitioned exchanges amount in defining a categorical variable (hard membership), together with canonical spectral scores, identical to Fisher’s discriminant functions. One demonstra作者: cardiac-arrest 時(shí)間: 2025-3-30 11:15
Recent Progress in Complex Network Analysis: Models of Random Intersection Graphs common neighbor. This tendency in theoretical random graph models is depicted by the asymptotically constant clustering coefficient. Moreover complex networks have power law degree distribution and small diameter (small world phenomena), thus these are desirable features of random graphs used for m作者: Immunization 時(shí)間: 2025-3-30 14:55 作者: jaunty 時(shí)間: 2025-3-30 18:38 作者: Heresy 時(shí)間: 2025-3-30 21:50 作者: Isometric 時(shí)間: 2025-3-31 02:17 作者: 希望 時(shí)間: 2025-3-31 07:34
On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Serieserlapping windows. It is a two-step strategy which performs at first, an on-line summarization by means of functional data structures, named Functional Boxplot micro-clusters; then, it reveals the final summarization by processing, off-line, the functional data structures. Our main contribute consis作者: ANT 時(shí)間: 2025-3-31 12:57 作者: 不妥協(xié) 時(shí)間: 2025-3-31 15:54
P2P RVM for Distributed Classificationetworks and support applications. Mining patterns in such distributed and dynamic environment is a challenging task, because centralization of data is not feasible. In this paper, we have proposed a distributed classification technique based on relevance vector machines (RVM) and local model exchang作者: onlooker 時(shí)間: 2025-3-31 20:37 作者: sebaceous-gland 時(shí)間: 2025-4-1 00:43 作者: Irrigate 時(shí)間: 2025-4-1 03:50
Incremental Weighted Naive Bays Classifiers for Data Streamiables (..) are assumed to be independent from the target variable (. ). Despite this strong assumption this classifier has proved to be very effective on many real applications and is often used on data stream for supervised classification. The naive Bayes classifier simply relies on the estimation
