Titlebook: Advances in Knowledge Discovery and Data Mining; 21st Pacific-Asia Co Jinho Kim,Kyuseok Shim,Yang-Sae Moon Conference proceedings 2017 Spri

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书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)




书目名称Advances in Knowledge Discovery and Data Mining影响因子(影响力)学科排名




书目名称Advances in Knowledge Discovery and Data Mining网络公开度




书目名称Advances in Knowledge Discovery and Data Mining网络公开度学科排名




书目名称Advances in Knowledge Discovery and Data Mining被引频次




书目名称Advances in Knowledge Discovery and Data Mining被引频次学科排名




书目名称Advances in Knowledge Discovery and Data Mining年度引用




书目名称Advances in Knowledge Discovery and Data Mining年度引用学科排名




书目名称Advances in Knowledge Discovery and Data Mining读者反馈




书目名称Advances in Knowledge Discovery and Data Mining读者反馈学科排名

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978-3-319-57453-0Springer International Publishing AG 2017

AMITY

Ray J. Hodgson,Howard J. Rankinertain individuals/communities or may be capable of inflicting harm to oneself or others. A search engine should regulate its query completion suggestions by detecting and filtering such queries as it may hurt the user sentiments or may lead to legal issues thereby tarnishing the brand image. Hence,

troponins

Cue Exposure and Relapse Preventionhalanobis metric learning methods that map both query (unlabeled) objects and labeled objects to new coordinates by a single transformation, our method learns a transformation of labeled objects to new points in the feature space whereas query objects are kept in their original coordinates. This met

OVERT

Behavioral Treatment of Binge Drinkingandomly dropping units and/or connections on each iteration of the training algorithm. Dropout and DropConnect are characteristic examples of such regularizers, that are widely popular among practitioners. However, a drawback of such approaches consists in the fact that their postulated probability

judiciousness

稀释前

Behavioral Treatment of Alcoholismabeled by multiple annotators has become a common scenario these days. Since annotators have different expertise, labels acquired from them might not be perfectly accurate. This paper derives an optimization framework to solve this task through estimating the expertise of each annotator and the labe

prolate

https://doi.org/10.1007/978-3-030-91526-1cross groups while respecting their idiosyncrasies. The model is built using techniques that are now considered standard in the statistical parameter estimation literature, namely, Hierarchical Dirichlet processes (HDP) and Hierarchical Generalized Linear Models (HGLM), and therefore, we name it “In

灰姑娘

Shannon C. Trotter,Suchita Sampathed implicitly by a set of training data used for ‘learning’ .. It is an important component for entity resolution, network link prediction, protein-protein interaction prediction, and so on. Although deep neural networks (DNNs) outperform other methods in many tasks and have thus attracted the atten

HEDGE

Shannon C. Trotter,Suchita Sampathn sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking