Titlebook: Advances in Knowledge Discovery and Data Mining; 22nd Pacific-Asia Co Dinh Phung,Vincent S. Tseng,Lida Rashidi Conference proceedings 2018

HAG

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




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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

Apogee

978-3-319-93033-6Springer International Publishing AG, part of Springer Nature 2018

arboretum

Dean D. T. Maglinte,Hans Herlingerremely time consuming and expensive. In this paper we propose strategies for estimating performance of a classifier using as little labeling resource as possible. Specifically, we assume a labeling budget is given and the goal is to get a good estimate of the classifier performance using the provide

PAEAN

Dean D. T. Maglinte,Hans Herlingerwith social data, such as the tweet stream generated by Twitter users in chronological order. A salient, and perhaps also the most interesting, feature of such user-generated content is its never-failing novelty, which, unfortunately, would challenge most traditional pre-trained classification model

macabre

Dean D. T. Maglinte,Hans Herlinger implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the

加劇

Münster Studies of Coronary Heart Diseaseassifier quality are crucial aspects of multi-label classification. In this paper, we propose a multi-structure SVM (called MSSVM) which allows the user to hypothesize multiple label interaction structures and helps to identify their importance in improving generalization performance. We design an e

ERUPT

Werner H. Hauss,Robert W. Wisslersemantic and syntactic features are well studied, global category information has been mostly ignored within the NN based framework. Samples with the same sentiment category should have similar vectors in represent space. Motivated by this, we propose a novel global sentiment centroids based neural

農(nóng)學(xué)

Fertilization and Embryo Culture,wever, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limited information for only one branch of the decision tree, resulting in insufficient accuracy and interpretability. Third, randomized

addict

土坯

Results from In Vitro Fertilization,eads to a classifier with a reject option, that allows the user to limit the number of erroneous predictions made on the test set, without any need to reveal the true labels of the test objects. The method described in this paper works by estimating the cumulative error count on a set of predictions