Storage Retrieval

Download Advanced Data Mining and Applications: 12th International by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng PDF

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By Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng

This publication constitutes the lawsuits of the twelfth foreign convention on complicated info Mining and purposes, ADMA 2016, held in Gold Coast, Australia, in December 2016.

The 70 papers awarded during this quantity have been conscientiously reviewed and chosen from one zero five submissions. the chosen papers lined a wide selection of significant subject matters within the quarter of knowledge mining, together with parallel and allotted facts mining algorithms, mining on facts streams, graph mining, spatial facts mining, multimedia information mining, net mining, the net of items, wellbeing and fitness informatics, and biomedical facts mining.

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Extra resources for Advanced Data Mining and Applications: 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings

Example text

2) were chosen into the prediction model. For example, for the affective category, negative, sadness, and anxiety were assigned large positive coefficients, whilst positive emotion had negative coefficient. The positive predictors also included negation, which was found in the preference of community blogs, confirming the finding in Sect. 2. Of personal concerns chosen into the model, only death was assigned positive coefficient, whilst the rest (work, leisure, home, money, and religion) had negative coefficients.

The confidence-weighted algorithm [13] has also been improved by introducing the adaptive regularization (AROW) that deals with inseparable data [10]. The soft confidenceweighted (SCW) algorithm improves upon AROW by maintaining an adaptive margin [35]. In this paper, we propose a novel framework that solves a linear online bipartite ranking using the soft confidence-weighted algorithm [35]. The proposed confidence-weighted bipartite ranking algorithm (CBR) entertains the fast training and testing phases of linear bipartite ranking.

The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010) 21. : Recognizing depression from Twitter activity. In: Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 3187–3196 (2015) 22. : Nonverbal cues for depth of depression: set versus no set. J. Consult. Clin. Psychol. edu Abstract. Bipartite ranking is a fundamental machine learning and data mining problem. It commonly concerns the maximization of the AUC metric.

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