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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書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)




書目名稱Advances in Knowledge Discovery and Data Mining影響因子(影響力)學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度




書目名稱Advances in Knowledge Discovery and Data Mining網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次




書目名稱Advances in Knowledge Discovery and Data Mining被引頻次學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining年度引用




書目名稱Advances in Knowledge Discovery and Data Mining年度引用學(xué)科排名




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)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

稀釋前

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