標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michele Berlingerio,Francesco Bonchi,Georgiana Ifr Conference p [打印本頁] 作者: CANTO 時間: 2025-3-21 18:20
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書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: 儀式 時間: 2025-3-21 21:01 作者: 死亡率 時間: 2025-3-22 02:21 作者: mutineer 時間: 2025-3-22 06:27 作者: 違法事實(shí) 時間: 2025-3-22 11:54 作者: 尖 時間: 2025-3-22 16:20 作者: 不近人情 時間: 2025-3-22 18:59 作者: 輕率看法 時間: 2025-3-22 23:39 作者: Apoptosis 時間: 2025-3-23 03:11
Temporally Evolving Community Detection and Prediction in Content-Centric Networksch temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurri作者: 賄賂 時間: 2025-3-23 08:39 作者: 范圍廣 時間: 2025-3-23 13:20 作者: 形狀 時間: 2025-3-23 14:31
Dynamic Hierarchies in Temporal Directed Networksven directed network can be formulated as follows: partition vertices into levels such that, ideally, there are only forward edges, that is, edges from upper levels to lower levels. In practice, the ideal case is impossible, so instead we minimize some penalty function on the backward edges. One pra作者: 灌溉 時間: 2025-3-23 20:32 作者: Morbid 時間: 2025-3-24 01:50 作者: 頌揚(yáng)本人 時間: 2025-3-24 02:28
Social-Affiliation Networks: Patterns and the , Modelat . do its structural properties such as edge or triangle counts follow, .? More challengingly, how can we synthetically generate networks which . satisfy those rules or patterns? Our work attempts to answer these closely-related questions in the context of the increasingly prevalent social-affilia作者: 競選運(yùn)動 時間: 2025-3-24 06:33 作者: sundowning 時間: 2025-3-24 13:00
Think Before You Discard: Accurate Triangle Counting in Graph Streams with Deletionswe obtain unbiased estimates with small variances?.Counting triangles (i.e., cliques of size three) in a graph is a classical problem with applications in a wide range of research areas, including social network analysis, data mining, and databases. Recently, streaming algorithms for triangle counti作者: myelography 時間: 2025-3-24 17:11 作者: expansive 時間: 2025-3-24 22:59 作者: 背帶 時間: 2025-3-25 00:32 作者: 嬰兒 時間: 2025-3-25 03:59 作者: 輕信 時間: 2025-3-25 10:39
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Boundsvide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Nevertheless, current hyperparameter tuning methods predominantly rely on expensive cross validation or heuristics that is not optimiz作者: 鞠躬 時間: 2025-3-25 11:43
Deep Learning Architecture Search by Neuro-Cell-Based Evolution with Function-Preserving Mutationst knowledge. We propose a novel neuro-evolutionary technique to solve this problem without human interference. Our method assumes that a convolutional neural network architecture is a sequence of neuro-cells and keeps mutating them using function-preserving operations. This novel combination of appr作者: Stable-Angina 時間: 2025-3-25 17:41
VC-Dimension Based Generalization Bounds for Relational Learningll fragment from some social network). In this paper we are particularly concerned with scenarios in which we can assume that (i) the domain elements appearing in the given sample have been uniformly sampled without replacement from the (unknown) full domain and (ii) the sample is complete for these作者: Anticoagulants 時間: 2025-3-25 22:03
Social-Affiliation Networks: Patterns and the , Modelelf-similarity, to . generate graphs obeying the observed patterns. Experiments show that: (i) the discovered rules are useful in detecting deviations as anomalies and (ii) . is fast and scales linearly with network size, producing graphs with millions of edges and attributes in only a few seconds. Code related to this paper is available at: ..作者: GUILT 時間: 2025-3-26 00:50
: Modeling the Co-evolution of Opinions and Network Connectionstence or dissolution of social ties. Using a unique real-world network dataset including periodic user surveys, we show that . performs with high accuracy, while outperforming the baseline approaches. Code related to this paper is available at: . and Data related to this paper is available at: ..作者: 討好女人 時間: 2025-3-26 06:43 作者: GROSS 時間: 2025-3-26 11:44
Fast and Provably Effective Multi-view Classification with Landmark-Based SVMsing-view scenario by only reconstructing the similarities to the landmarks. Empirical results, both in complete and missing view settings, highlight the superior performances of our method, in terms of accuracy and execution time, w.r.t. state of the art techniques. Code related to this paper is available at: ..作者: 用不完 時間: 2025-3-26 15:50 作者: 情愛 時間: 2025-3-26 20:28
0302-9743 ledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.?. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.?.The contribut作者: 放棄 時間: 2025-3-27 00:25
Nystr?m-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descentxploiting the low-rank structure of a Nystr?m kernel approximation. Our experiments suggest that the Nystr?m-SGD enables us to rapidly train high-accuracy classifiers for large-scale classification tasks. Code related to this paper is available at: ..作者: 平息 時間: 2025-3-27 04:37
Conference proceedings 2019overy in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.?. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track.?.The contributions were 作者: Morphine 時間: 2025-3-27 06:06 作者: LAVE 時間: 2025-3-27 11:50
VC-Dimension Based Generalization Bounds for Relational Learning domain elements (i.e. it is the full substructure induced by these elements). Within this setting, we study bounds on the error of sufficient statistics of relational models that are estimated on the available data. As our main result, we prove a bound based on a variant of the Vapnik-Chervonenkis dimension which is suitable for relational data.作者: Triglyceride 時間: 2025-3-27 15:01
978-3-030-10927-1Springer Nature Switzerland AG 2019作者: grounded 時間: 2025-3-27 18:13
Machine Learning and Knowledge Discovery in Databases978-3-030-10928-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 輕推 時間: 2025-3-28 00:01 作者: 不可磨滅 時間: 2025-3-28 05:59 作者: CODA 時間: 2025-3-28 07:14
Kijung Shin,Jisu Kim,Bryan Hooi,Christos Faloutsosd Ergebnisse ? Pr?vention von Mobbing.Die Autorin.M.A. Melanie Burger studierte Sozialp?dagogik an der Karl-Franzens-Universit?t Graz und arbeitet derzeit als Erzieherin an einer Privatschule in Graz.?.978-3-658-28682-8978-3-658-28683-5Series ISSN 2512-1081 Series E-ISSN 2512-109X 作者: 雄辯 時間: 2025-3-28 11:57 作者: Infusion 時間: 2025-3-28 16:48
d Ergebnisse ? Pr?vention von Mobbing.Die Autorin.M.A. Melanie Burger studierte Sozialp?dagogik an der Karl-Franzens-Universit?t Graz und arbeitet derzeit als Erzieherin an einer Privatschule in Graz.?.978-3-658-28682-8978-3-658-28683-5Series ISSN 2512-1081 Series E-ISSN 2512-109X 作者: exophthalmos 時間: 2025-3-28 22:31
Magda Gregorová,Jason Ramapuram,Alexandros Kalousis,Stéphane Marchand-Mailletd Ergebnisse ? Pr?vention von Mobbing.Die Autorin.M.A. Melanie Burger studierte Sozialp?dagogik an der Karl-Franzens-Universit?t Graz und arbeitet derzeit als Erzieherin an einer Privatschule in Graz.?.978-3-658-28682-8978-3-658-28683-5Series ISSN 2512-1081 Series E-ISSN 2512-109X 作者: intercede 時間: 2025-3-29 00:57
Ana Paula Appel,Renato L. F. Cunha,Charu C. Aggarwal,Marcela Megumi Terakado作者: Nausea 時間: 2025-3-29 05:31
Carl Yang,Mengxiong Liu,Frank He,Xikun Zhang,Jian Peng,Jiawei Han作者: 口訣 時間: 2025-3-29 08:42 作者: bronchiole 時間: 2025-3-29 12:03 作者: 先行 時間: 2025-3-29 16:08 作者: Canopy 時間: 2025-3-29 22:00
Aastha Nigam,Kijung Shin,Ashwin Bahulkar,Bryan Hooi,David Hachen,Boleslaw K. Szymanski,Christos Falo作者: 血統(tǒng) 時間: 2025-3-29 23:52
ein Modell, das Evaluation, Qualifikation und Weiterbildung in angemessener Weise verbindet, d.h. eingebunden ist in einen Kontext, in dem Hochschullehrer selbstbestimmt die eigenen Lehrleistungen dokumentieren und bewerten und diesen Prozess zugleich als selbstgesteuerte Verbesserung und Weiterent作者: 凝視 時間: 2025-3-30 06:01 作者: sinoatrial-node 時間: 2025-3-30 12:15 作者: DOLT 時間: 2025-3-30 12:30 作者: ODIUM 時間: 2025-3-30 20:23
Conference proceedings 2019lysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning.?. Part III: ADS data science applications; ADS e-commerce; ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track..作者: GET 時間: 2025-3-30 22:01
Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,作者: novelty 時間: 2025-3-31 02:40
Michele Berlingerio,Francesco Bonchi,Georgiana Ifr作者: interior 時間: 2025-3-31 07:51
Temporally Evolving Community Detection and Prediction in Content-Centric Networksonal form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence o作者: expound 時間: 2025-3-31 12:33
Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structuredurcation points in the topology underlying the data. It then uses this information to piece together a graph that is homeomorphic to the unknown one-dimensional stratified space underlying the point cloud data. We evaluate our method on a number of artificial and real-life data sets, demonstrating i作者: 地名詞典 時間: 2025-3-31 15:06
Similarity Modeling on Heterogeneous Networks via Automatic Path Discoveryiscover useful paths for pairs of nodes under both structural and content information. To this end, we combine continuous reinforcement learning and deep content embedding into a novel semi-supervised joint learning framework. Specifically, the supervised reinforcement learning component explores us
