Download Data Mining and Big Data: First International Conference, by Ying Tan, Yuhui Shi PDF

By Ying Tan, Yuhui Shi

The LNCS quantity LNCS 9714 constitutes the refereed lawsuits of the foreign convention on facts Mining and massive info, DMBD 2016, held in Bali, Indonesia, in June 2016.
The fifty seven papers offered during this quantity have been conscientiously reviewed and chosen from a hundred and fifteen submissions. The subject matter of DMBD 2016 is "Serving existence with information Science". facts mining refers back to the job of facing gigantic facts units to seem for appropriate or pertinent information.The papers are prepared in 10 cohesive sections masking all significant themes of the learn and improvement of information mining and massive facts and one Workshop on Computational facets of trend acceptance and computing device Vision.

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Extra info for Data Mining and Big Data: First International Conference, DMBD 2016, Bali, Indonesia, June 25-30, 2016. Proceedings

Example text

MH, a “pivot term” will be selected if the term has been found harder to classified by previous classifiers in each iteration. MH, called MP-Boost, is proposed by Esuli et at. [3]. MH. This mechanism outperforms in effectiveness and efficiency. c Springer International Publishing Switzerland 2016 Y. Tan and Y. ): DMBD 2016, LNCS 9714, pp. 27–37, 2016. 1007/978-3-319-40973-3 3 28 F. Gai et al. Both methods mentioned above have to scan the whole feature space to select the pivot term or terms, which is obviously sensitive to the number of features.

In each iteration, AdaBoost will enhance the performance depending on the accuracy of previous classifiers. Ferreira and Figueiredo [4] review the AdaBoost algorithm in details, and its variants have been exploited in diverse domains such as text categorization, face detection, remote sensing image detection, barcode recognition, and banknote number recognition [15]. MH [11] is an extension of AdaBoost to be fit for multi-class multi-label classification. MH, a “pivot term” will be selected if the term has been found harder to classified by previous classifiers in each iteration.

Table 1 contains data on average weekly numbers of vacancies and resumes (so that only unique items are considered, not repeating postings). The official data on the regions’ urban population is for the 1st of January, 2015. The data for 2015 was also collected and will be analyzed and subsequently made available with the approvement by the system’s management. Table 1. Average weekly numbers of vacancies and resumes per regions, 2014. Region Krasnoyarski krai Kemerovsk. obl. Novosibirsk. obl. Omskaya obl.

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