標(biāo)題: Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Alípio Jor Conference p [打印本頁(yè)] 作者: Stenosis 時(shí)間: 2025-3-21 17:20
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases被引頻次
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases被引頻次學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases年度引用
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases年度引用學(xué)科排名
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋
書(shū)目名稱Machine Learning and Knowledge Discovery in Databases讀者反饋學(xué)科排名
作者: exorbitant 時(shí)間: 2025-3-21 22:46
Yuanli Pei,Li-Ping Liu,Xiaoli Z. Ferns Fragebogen nicht direkt die Lernpr?ferenzen von Probanden, sondern eher generelle Verhaltenstendenzen abfragt. In einer ersten revidierten Version im Jahre 1986 wurden britische Redensarten durch allgemein verst?ndlichere ersetzt, damit der Lernstilfragebogen auch au?erhalb des Vereinigten K?nigsr作者: incision 時(shí)間: 2025-3-22 03:23
Markus Ring,Florian Otto,Martin Becker,Thomas Niebler,Dieter Landes,Andreas Hothorimentellen Studie ein und geht dem Einfluss unterschiedlicher Interaktivit?tsgrade von Lernprogrammen auf den Lernerfolg von Nutzern mit unterschiedlichem Lernstil nach. Es wird deutlich, dass sich unabh?ngig vom pers?nlichen Lernstil und dem Interaktivit?tsgrad eines Lernprogramms immer ein Lernerfolg einst978-3-8350-0363-7978-3-8350-9212-9作者: 謙虛的人 時(shí)間: 2025-3-22 08:38 作者: Conduit 時(shí)間: 2025-3-22 09:49 作者: 音樂(lè)等 時(shí)間: 2025-3-22 12:54
Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representation method for constructing robust classifiers..We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.作者: 報(bào)復(fù) 時(shí)間: 2025-3-22 19:27
Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Can of adjustment loss from calibration loss requires extra assumptions which we prove to be satisfied for the most frequently used proper scoring rules: Brier score and log-loss. We propose algorithms to perform adjustment as a simpler alternative to calibration.作者: 山羊 時(shí)間: 2025-3-22 22:42 作者: HAWK 時(shí)間: 2025-3-23 05:17
Versatile Decision Trees for Learning Over Multiple Contexts Versatile Model that is rich enough to handle different kinds of shift without making strong assumptions such as linearity, and furthermore does not require labelled data to identify the data shift at deployment. Empirical results on both synthetic shift and real datasets shift show strong performa作者: exhilaration 時(shí)間: 2025-3-23 07:38 作者: ascend 時(shí)間: 2025-3-23 10:33 作者: Indict 時(shí)間: 2025-3-23 16:45
Martin Ratajczak,Sebastian Tschiatschek,Franz Pernkopfernst Du schnell und sicher: Schnell wie mit einem ?Skript“ – und sicher, weil alles von den Experten auf Herz und Nieren geprüft wurde. – An echten Prüfungen getestet! Für Psychologiestudierende im Bachelorstudium und Nebenfachstudierende.978-3-662-43563-2978-3-662-44941-7Series ISSN 0937-7433 Series E-ISSN 2512-5214 作者: flourish 時(shí)間: 2025-3-23 18:59
Reem Al-Otaibi,Ricardo B. C. Prudêncio,Meelis Kull,Peter Flachernst Du schnell und sicher: Schnell wie mit einem ?Skript“ – und sicher, weil alles von den Experten auf Herz und Nieren geprüft wurde. – An echten Prüfungen getestet! Für Psychologiestudierende im Bachelorstudium und Nebenfachstudierende.978-3-662-43563-2978-3-662-44941-7Series ISSN 0937-7433 Series E-ISSN 2512-5214 作者: Lament 時(shí)間: 2025-3-24 01:12 作者: AGOG 時(shí)間: 2025-3-24 02:50
Wojciech Marian Czarnecki,Rafal Jozefowicz,Jacek Tabor作者: Receive 時(shí)間: 2025-3-24 09:08 作者: 罵人有污點(diǎn) 時(shí)間: 2025-3-24 11:57
Yutaro Shigeto,Ikumi Suzuki,Kazuo Hara,Masashi Shimbo,Yuji Matsumoto作者: 定點(diǎn) 時(shí)間: 2025-3-24 18:53
Andrea Dal Pozzolo,Olivier Caelen,Gianluca Bontempi作者: Hemiparesis 時(shí)間: 2025-3-24 21:05
Conference proceedings 2015d 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..作者: MIRE 時(shí)間: 2025-3-25 02:12
0302-9743 ata; matrix and tensor analysis; pattern 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..978-3-319-23527-1978-3-319-23528-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: abduction 時(shí)間: 2025-3-25 05:22 作者: 放逐某人 時(shí)間: 2025-3-25 10:38
Fast Label Embeddings via Randomized Linear Algebrasettings. The result is a randomized algorithm whose running time is exponentially faster than naive algorithms. We demonstrate our techniques on two large-scale public datasets, from the Large Scale Hierarchical Text Challenge and the Open Directory Project, where we obtain state of the art results.作者: perjury 時(shí)間: 2025-3-25 13:06
Regression with Linear Factored FunctionsWe derive a regularized greedy optimization scheme, that learns factored basis functions during training. The novel regression algorithm performs competitively to . on benchmark tasks, and the learned LFF functions are with 4-9 factored basis functions on average very compact.作者: Apoptosis 時(shí)間: 2025-3-25 18:32
Ridge Regression, Hubness, and Zero-Shot Learningprove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.作者: 懶惰人民 時(shí)間: 2025-3-25 23:59 作者: Erythropoietin 時(shí)間: 2025-3-26 01:29 作者: 安撫 時(shí)間: 2025-3-26 07:30 作者: 富足女人 時(shí)間: 2025-3-26 09:33 作者: Customary 時(shí)間: 2025-3-26 14:02
ConDist: A Context-Driven Categorical Distance Measureal distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.作者: gangrene 時(shí)間: 2025-3-26 17:23
Conference proceedings 2015overy in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. .The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were作者: blackout 時(shí)間: 2025-3-26 23:27 作者: elastic 時(shí)間: 2025-3-27 04:11
Discovering Opinion Spammer Groups by Network Footprintsammers on a 2-hop subgraph induced by top ranking products. We demonstrate the efficiency and effectiveness of our approach on both synthetic and real-world datasets from two different domains with millions of products and reviewers. Moreover, we discover interesting strategies that spammers employ through case studies of our detected groups.作者: 物種起源 時(shí)間: 2025-3-27 07:49 作者: ARCH 時(shí)間: 2025-3-27 10:48 作者: Fortuitous 時(shí)間: 2025-3-27 16:22
Ehsan Amid,Aristides Gionis,Antti Ukkoneney ein, um die bis dahin unbeachtete Frage, wie Führungskr?fte lemen, mit ihm n?her zu er?rtem. Als Ausgangspunkt w?hlten sie . (LSI) (1999a), welches zum damaligen Zeitpunkt das einzig verfügbare Messinstrument zur Untersuchung und Erhebung von individuellen Unterschieden im Lemverhalten war. Das L作者: 嬉耍 時(shí)間: 2025-3-27 18:25 作者: Parley 時(shí)間: 2025-3-27 22:52
Markus Ring,Florian Otto,Martin Becker,Thomas Niebler,Dieter Landes,Andreas HothoLerninhalten im Mittelpunkt, doch zeigte sich bald, dass nicht jeder Versuch, Informations- und Kommunikationstechnologien im Aus- und Weiterbildungsbereich einzusetzen, alle M?glichkeiten vollst?ndig aussch?pft. Wichtig ist, die Nutzerperspektive differenziert zu berücksichtigen. ..Daniel Staemmler作者: ARBOR 時(shí)間: 2025-3-28 04:52 作者: 和平 時(shí)間: 2025-3-28 09:54 作者: 跑過(guò) 時(shí)間: 2025-3-28 11:42 作者: 下級(jí) 時(shí)間: 2025-3-28 17:45 作者: chapel 時(shí)間: 2025-3-28 19:48
Maximum Entropy Linear Manifold for Learning Discriminative Low-Dimensional Representationticular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis..In this paper we propose Maximum Entrop作者: Enrage 時(shí)間: 2025-3-28 23:28 作者: 修剪過(guò)的樹(shù)籬 時(shí)間: 2025-3-29 03:45
Parameter Learning of Bayesian Network Classifiers Under Computational Constraintsrizing the BNCs are represented by low bit-width fixed-point numbers. In contrast to previous work, we analyze the learning of these parameters using reduced-precision arithmetic only which is important for computationally constrained platforms, e.g. embedded- and ambient-systems, as well as power-a作者: evasive 時(shí)間: 2025-3-29 10:46
Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning of learning underlying structures over labels is to project both instances and labels into the same space where an instance and its relevant labels tend to have similar representations. In this paper, we present a novel method to learn a joint space of instances and labels by leveraging a hierarchy作者: omnibus 時(shí)間: 2025-3-29 15:22
Regression with Linear Factored Functionsis paper introduces a novel .-algorithm that learns . (LFF). This class of functions has structural properties that allow to analytically solve certain integrals and to calculate point-wise products. Applications like . and . can exploit these properties to break the curse and speed up computation. 作者: Platelet 時(shí)間: 2025-3-29 17:20
Ridge Regression, Hubness, and Zero-Shot Learningel space. Contrary to the existing approach, which attempts to find a mapping from the example space to the label space, we show that mapping labels into the example space is desirable to suppress the emergence of hubs in the subsequent nearest neighbor search step. Assuming a simple data model, we 作者: 保留 時(shí)間: 2025-3-29 22:21 作者: 翻動(dòng) 時(shí)間: 2025-3-30 01:18
Structured Regularizer for Neural Higher-Order Sequence Modelsence modelling. We show that this regularizer can be derived as lower bound from a mixture of models sharing parts, e.g. neural sub-networks, and relate it to ensemble learning. Furthermore, it can be expressed explicitly as regularization term in the training objective..We exemplify its effectivene作者: FRAUD 時(shí)間: 2025-3-30 04:30
Versatile Decision Trees for Learning Over Multiple Contextss can vary significantly when they are learned and deployed in different contexts with different data distributions. In the literature, this phenomenon is called dataset shift. In this paper, we address several important issues in the dataset shift problem. First, how can we automatically detect tha作者: 無(wú)節(jié)奏 時(shí)間: 2025-3-30 12:15
When is Undersampling Effective in Unbalanced Classification Tasks? learning of the classifier. Though this seems to work for the majority of cases, no detailed analysis exists about the impact of undersampling on the accuracy of the final classifier. This paper aims to fill this gap by proposing an integrated analysis of the two elements which have the largest imp作者: UNT 時(shí)間: 2025-3-30 15:41
A Kernel-Learning Approach to Semi-supervised Clustering with Relative Distance Comparisonsween the data items. The additional constraints are meant to reflect side-information that is not expressed in the feature vectors, directly. Relative comparisons can express structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints that are commonly used for semi-supe作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-30 18:35 作者: 結(jié)束 時(shí)間: 2025-3-31 00:46 作者: 不可思議 時(shí)間: 2025-3-31 03:51 作者: 山羊 時(shí)間: 2025-3-31 07:03
978-3-319-23527-1Springer International Publishing Switzerland 2015作者: goodwill 時(shí)間: 2025-3-31 09:29
Machine Learning and Knowledge Discovery in Databases978-3-319-23528-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 龍卷風(fēng) 時(shí)間: 2025-3-31 13:57
Annalisa Appice,Pedro Pereira Rodrigues,Alípio JorIncludes supplementary material: 作者: impaction 時(shí)間: 2025-3-31 18:59
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/m/image/620513.jpg作者: legitimate 時(shí)間: 2025-4-1 00:09
https://doi.org/10.1007/978-3-319-23528-8data mining; foundations of machine learning and data mining; knowledge discovery in databases; probabi作者: 喚醒 時(shí)間: 2025-4-1 03:12
