標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa Conference p [打印本頁] 作者: HAG 時(shí)間: 2025-3-21 16:13
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書目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名
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書目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名
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書目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名
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: Lymphocyte 時(shí)間: 2025-3-21 22:21 作者: 合并 時(shí)間: 2025-3-22 00:26 作者: 尋找 時(shí)間: 2025-3-22 06:56
Machine Learning and Knowledge Discovery in Databases978-3-319-23525-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 吸引力 時(shí)間: 2025-3-22 11:04 作者: 和平 時(shí)間: 2025-3-22 16:05
Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond cus is on the computational aspects of the theory and studying the computational complexity and approximability of many problems related to generalized matrix factorizations. The results immediately apply to a large number of data mining problems, and hopefully allow generalizing future results and algorithms, as well.作者: 利用 時(shí)間: 2025-3-22 19:54 作者: Sinus-Rhythm 時(shí)間: 2025-3-23 01:15 作者: GUISE 時(shí)間: 2025-3-23 04:28
Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithmsifs underlying the kernel predictor. We demonstrate the efficacy of our approach through a series of experiments on synthetic and real data, including problems from handwritten digit recognition and a large-scale . splice site data set from the domain of computational biology.作者: LEER 時(shí)間: 2025-3-23 06:49 作者: Neuropeptides 時(shí)間: 2025-3-23 10:36
Non-parametric Jensen-Shannon Divergencerom data without the need for estimation. Moreover, empirical evaluation shows that our method performs very well in detecting differences between distributions, outperforming the state of the art in both statistical power and efficiency for a wide range of tasks.作者: AGOG 時(shí)間: 2025-3-23 14:57
Swap Randomization of Bases of Sequences for Mining Satellite Image Times Seriessing an entropy-based measure, the locations obtained on the actual dataset and a single swap randomized dataset are compared. The potential and generality of the proposed approach is evidenced by experiments on both optical and radar SITS.作者: 陳腐思想 時(shí)間: 2025-3-23 20:38
Fast Training of Support Vector Machines for Survival Analysisover existing training algorithms, which fail due to their inherently high time and space complexities when applied to large datasets. We validate the proposed survival models on 6 real-world datasets, and show that pure ranking-based approaches outperform regression and hybrid models.作者: prostatitis 時(shí)間: 2025-3-24 01:13 作者: crockery 時(shí)間: 2025-3-24 03:50 作者: filicide 時(shí)間: 2025-3-24 08:21 作者: kyphoplasty 時(shí)間: 2025-3-24 11:54 作者: 要塞 時(shí)間: 2025-3-24 16:46
Dyad Ranking Using A Bilinear Plackett-Luce Modele an extension of an existing label ranking method based on the Plackett-Luce model, a statistical model for rank data. Moreover, we present first experimental results confirming the usefulness of the additional information provided by the feature description of alternatives.作者: HUMID 時(shí)間: 2025-3-24 20:04 作者: FIS 時(shí)間: 2025-3-25 03:07 作者: 盡管 時(shí)間: 2025-3-25 03:55
Aleksey Buzmakov,Sergei O. Kuznetsov,Amedeo Napoli作者: 關(guān)心 時(shí)間: 2025-3-25 09:53 作者: Irksome 時(shí)間: 2025-3-25 14:14
oder Berater), der ?lter als der Betreute ist; ein Rollentausch ist dabei meist nicht vorgesehen (Hatano & Inagaki, 1991: vertikale Interaktion). Im folgenden wird zun?chst über kooperative Lernformen berichtet, in denen Lernen durch Lehren eine zentrale Rolle spielt. Im Anschlu? daran wird ein Einb作者: jungle 時(shí)間: 2025-3-25 19:42 作者: 收到 時(shí)間: 2025-3-25 20:30
Mathieu Blondel,Akinori Fujino,Naonori Uedaren, da? die biographische Zukunft düster wird und sich zeitweise sogar zu verschlie?en scheint. Wie ich im n?chsten Kapitel zeigen m?chte, kann man diese Zust?nde in Anlehnung an die ph?nomenologische Soziologie von Alfred Schütz als Erleiden an einer erh?hten Erlebniskomplexit?t verstehen, welches作者: Lipohypertrophy 時(shí)間: 2025-3-26 03:15
Pauli Miettinenhweite. Mitunter ist das Lernen intensiv, insofern die Betroffenen eigene biographische Entscheidungen für ihre Leiden verantwortlich machen. Unter diesen Umst?nden kann das Leiden sogar als Faktor für eine Individualisierung verstanden werden. Um Zusammenh?nge zwischen Leiden und Lernen zu erkennen作者: 窒息 時(shí)間: 2025-3-26 06:59
Changwei Hu,Piyush Rai,Changyou Chen,Matthew Harding,Lawrence Carinhst sind freiwillige T?tigkeiten in Vereinen schon rein quantitativ von Interesse. So geht eine repr?sentative Studie zum freiwilligen Engagement in Deutschland von einer Engagementquote von 36% aus, die sich darüber hinaus zwischen 1999 und 2004 um 2 Prozentpunkte erh?ht hat (Gensicke, Picot & Geis作者: 細(xì)胞 時(shí)間: 2025-3-26 11:25
Van-Tinh Tran,Alex Aussemdar. Zun?chst sind freiwillige T?tigkeiten in Vereinen schon rein quantitativ von Interesse. So geht eine repr?sentative Studie zum freiwilligen Engagement in Deutschland von einer Engagementquote von 36% aus, die sich darüber hinaus zwischen 1999 und 2004 um 2 Prozentpunkte erh?ht hat (Gensicke, Picot & Geis978-3-531-15963-8978-3-531-91020-8作者: 轉(zhuǎn)換 時(shí)間: 2025-3-26 14:19 作者: 動(dòng)物 時(shí)間: 2025-3-26 16:48 作者: 波動(dòng) 時(shí)間: 2025-3-26 22:49
Kailash Budhathoki,Jilles Vreeken. Dabei geht es zuerst um das Grundprinzip neuer Führung – der übernahme von Verantwortung für sich, die Mitarbeiter und die Organisation. Dann werden die Elemente von New Leadership mit den Kompetenzen eines New Leaders zusammen vorgestellt. Was Führen in Krisensituationen hei?t, wird eigens themat作者: FOIL 時(shí)間: 2025-3-27 01:41
ehmen immer noch ein Nischendasein führt. Innovatoren haben deshalb die Chance auf eine führende Positionierung im Markt – vorausgesetzt, die Komplexit?t des Entwicklungs- und Implementierungsprozesses kann bew?ltigt werden. Der vorliegende Beitrag liefert dazu konkrete Vorschl?ge, wie die Transform作者: maladorit 時(shí)間: 2025-3-27 05:26
Dirk Sch?fer,Eyke Hüllermeier. Dabei geht es zuerst um das Grundprinzip neuer Führung – der übernahme von Verantwortung für sich, die Mitarbeiter und die Organisation. Dann werden die Elemente von New Leadership mit den Kompetenzen eines New Leaders zusammen vorgestellt. Was Führen in Krisensituationen hei?t, wird eigens themat作者: Offset 時(shí)間: 2025-3-27 10:46
Sebastian P?lsterl,Nassir Navab,Amin Katouzianehmen immer noch ein Nischendasein führt. Innovatoren haben deshalb die Chance auf eine führende Positionierung im Markt – vorausgesetzt, die Komplexit?t des Entwicklungs- und Implementierungsprozesses kann bew?ltigt werden. Der vorliegende Beitrag liefert dazu konkrete Vorschl?ge, wie die Transform作者: pacifist 時(shí)間: 2025-3-27 17:08 作者: Carbon-Monoxide 時(shí)間: 2025-3-27 20:19
Conference proceedings 2015ence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.作者: Grievance 時(shí)間: 2025-3-27 22:38
0302-9743 n and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.978-3-319-23524-0978-3-319-23525-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 叫喊 時(shí)間: 2025-3-28 05:32
Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,作者: FOIL 時(shí)間: 2025-3-28 10:17
Annalisa Appice,Pedro Pereira Rodrigues,Carlos Soa作者: 溺愛 時(shí)間: 2025-3-28 12:45 作者: 復(fù)習(xí) 時(shí)間: 2025-3-28 15:28
Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optiy done on the current data set..Pruning as a new component for SMBO is an orthogonal contribution but nevertheless we compare it to surrogate models that learn across data sets and extensively investigate the impact of pruning with and without initialization for various state of the art surrogate mo作者: 蒸發(fā) 時(shí)間: 2025-3-28 21:46
Multi-Task Learning with Group-Specific Feature Space Sharinge descent, which employs a consensus-form Alternating Direction Method of Multipliers algorithm to optimize the Multiple Kernel Learning weights and, hence, to determine task affinities. Empirical evaluation on seven data sets exhibits a statistically significant improvement of our framework’s resul作者: scotoma 時(shí)間: 2025-3-29 02:50
Superset Learning Based on Generalized Loss Minimizationng technique for the problem of label ranking, in which the output space consists of all permutations of a fixed set of items. The label ranking method thus obtained is compared to existing approaches tackling the same problem.作者: 紀(jì)念 時(shí)間: 2025-3-29 05:25 作者: 索賠 時(shí)間: 2025-3-29 08:27 作者: capillaries 時(shí)間: 2025-3-29 12:21
Generalized Matrix Factorizations as a Unifying Framework for Pattern Set Mining: Complexity Beyond ted for data mining utilizes the fact that a matrix product can be interpreted as a sum of rank-1 matrices. Then the factorization of a matrix becomes the task of finding a small number of rank-1 matrices, sum of which is a good representation of the original matrix. Seen this way, it becomes obviou作者: 織物 時(shí)間: 2025-3-29 16:36
Scalable Bayesian Non-negative Tensor Factorization for Massive Count Dataonline) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a reparameterization of the Poisson distribution as a multinomial facilitates conjugacy in the model and enables simple and efficient Gi作者: Basal-Ganglia 時(shí)間: 2025-3-29 20:58
A Practical Approach to Reduce the Learning Bias Under Covariate Shiftand the target domains while the conditional distributions of the target Y given X are the same. A common technique to deal with this problem, called importance weighting, amounts to reweighting the training instances in order to make them resemble the test distribution. However this usually comes a作者: 榮幸 時(shí)間: 2025-3-30 03:04 作者: 尖牙 時(shí)間: 2025-3-30 07:58
Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optirs faster and even achieve better final performance. Sequential model-based optimization (SMBO) is the current state of the art framework for automatic hyperparameter optimization. Currently, it consists of three components: a surrogate model, an acquisition function and an initialization technique.作者: 尖酸一點(diǎn) 時(shí)間: 2025-3-30 10:31
Multi-Task Learning with Group-Specific Feature Space Sharingzation performance. (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultaneously to offer improved performance. We propose a novel Multi-Task Multiple Kernel Learning framework based on Support Vector Machines for binary clas作者: MERIT 時(shí)間: 2025-3-30 15:36 作者: AVANT 時(shí)間: 2025-3-30 19:16 作者: Ingrained 時(shí)間: 2025-3-30 23:44 作者: 珍奇 時(shí)間: 2025-3-31 04:22 作者: Canyon 時(shí)間: 2025-3-31 08:54 作者: Osteoporosis 時(shí)間: 2025-3-31 12:14 作者: languor 時(shí)間: 2025-3-31 15:25
Fast Training of Support Vector Machines for Survival Analysisdical research. When applied to large amounts of patient data, efficient optimization routines become a necessity. We propose efficient training algorithms for three kinds of linear survival support vector machines: 1) ranking-based, 2) regression-based, and 3) combined ranking and regression. We pe
