Titlebook: Advances in Knowledge Discovery and Data Mining; 20th Pacific-Asia Co James Bailey,Latifur Khan,Ruili Wang Conference proceedings 2016 Spri

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書(shū)目名稱Advances in Knowledge Discovery and Data Mining被引頻次




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書(shū)目名稱Advances in Knowledge Discovery and Data Mining讀者反饋




書(shū)目名稱Advances in Knowledge Discovery and Data Mining讀者反饋學(xué)科排名

FUME

978-3-319-31752-6Springer International Publishing Switzerland 2016

dithiolethione

https://doi.org/10.1007/978-1-4471-7293-2g problems, JCHL uses a single feature space to jointly classify multiple classification tasks with heterogeneous labels. For instance, biologists usually have to label the gene expression images with developmental stages and simultaneously annotate their anatomical terms. We would like to classify

跑過(guò)

Giacomo Vivanti,Melanie Pellecchiaesentatives of sampling methods, undersampling and oversampling cannot outperform each other. That is, undersampling fits some data sets while oversampling fits some other. Besides, the sampling rate also significantly influences the performance of a classifier, while existing methods usually adopt

固執(zhí)點(diǎn)好

Kristen Bottema-Beutel,Shannon Crowleyexity and sparsity levels of the solution. Addressing the sparsity is important to improve learning generalization, prediction accuracy and computational speedup. In this paper, we employ the max-margin principle and sparse approach to propose a new Sparse AMM (SAMM). We solve the new optimization o

Highbrow

https://doi.org/10.1007/978-3-031-04927-9, and relational connections between users in review systems. Although these methods can successfully identify spam activities, evolving fraud strategies can successfully escape from the detection rules by purchasing positive comments from massive random users, i.e., user Cloud. In this paper, we st

Cabinet

Clinical Guide to Exposure Therapytive models such as SVM. However, extra difficulties do arise in optimizing non-convex learning objectives and selecting multiple hyperparameters. Observing that many variations of large margin learning could be reformulated as jointly minimizing a parameterized quadratic objective, in this paper we

Habituate

https://doi.org/10.1007/978-3-031-04927-9sion is to complementally generate base models and elaborately combine their outputs. Traditionally, the weighted average of the outputs is treated as the final prediction. This means each base model plays a constant role in the whole data space. In fact, we know the predictive accuracy of each base

Hallowed

Clinical Guide to Exposure Therapyh test pattern with some predefined probability. In order to fully utilize the predictions provided by a conformal classifier, it is essential that those predictions are reliable, i.e., that a user is able to assess the quality of the predictions made. Although conformal classifiers are statisticall

結(jié)合

https://doi.org/10.1007/978-3-031-04927-9ed degree pathways to facilitate successful and timely graduation. This paper presents future-course grade predictions methods based on sparse linear models and low-rank matrix factorizations that are specific to each course or student-course tuple. These methods identify the predictive subsets of p