LEADER 01831nam 2200373 n 450 001 996385740003316 005 20200824121606.0 035 $a(CKB)4940000000071486 035 $a(EEBO)2240866304 035 $a(UnM)99836036e 035 $a(UnM)99836036 035 $a(EXLCZ)994940000000071486 100 $a19900823d1616 uy | 101 0 $alat 135 $aurbn||||a|bb| 200 00$aApologia veritatis illucescentis, pro auro potabili: seu essentia auri ad medicinalem potabilitatem absque corrosiuis reducti$b[electronic resource] $evt fere? omnibus humani corporis ęgritudinibus, ac pręsertim cordis corroborationi, tanquam vniversalis medicina, vtilissime? adhiberi possit; vna? cum rationibus intelligibilibus, testimonijs locupletissimis, et modo conuenienti in singulis morbis vsurpandi, producta: authore Francisco Antonio .. 210 $aLondini $cExcusum per Iohannem Legatt$d1616 215 $a[10], 110, [2] p. ; 4to 300 $aA translation, with additions, of: The apologie, or defence of a verity heretofore published concerning a medicine called aurum potabile. 300 $aThe title probably refers to his "Medicinae chymicae, et veri potabilis auri assertio", 1610. 300 $aThe first leaf and the last leaf are blank. 300 $aReproduction of the original in Cambridge University Library. 330 $aeebo-0021 606 $aGold$xTherapeutic use$vEarly works to 1800 615 0$aGold$xTherapeutic use 700 $aAnthony$b Francis$f1550-1623.$0938316 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996385740003316 996 $aApologia veritatis illucescentis, pro auro potabili: seu essentia auri ad medicinalem potabilitatem absque corrosiuis reducti$92310583 997 $aUNISA LEADER 04056nam 22005655 450 001 9910768172903321 005 20240312140702.0 010 $a981-9976-57-X 024 7 $a10.1007/978-981-99-7657-7 035 $a(MiAaPQ)EBC30979404 035 $a(Au-PeEL)EBL30979404 035 $a(CKB)29126986800041 035 $a(DE-He213)978-981-99-7657-7 035 $a(EXLCZ)9929126986800041 100 $a20231129d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDirty Data Processing for Machine Learning /$fby Zhixin Qi, Hongzhi Wang, Zejiao Dong 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (141 pages) 311 08$aPrint version: Qi, Zhixin Dirty Data Processing for Machine Learning Singapore : Springer Singapore Pte. Limited,c2024 9789819976560 327 $aChapter 1. Introduction -- Chapter 2. Impacts of Dirty Data on Classification and Clustering Models -- Chapter 3. Dirty-Data Impacts on Regression Models -- Chapter 4. Incomplete Data Classification with View-Based Decision Tree -- Chapter 5. Density-Based Clustering for Incomplete Data -- Chapter 6. Feature Selection on Inconsistent Data -- Chapter 7. Cost-Sensitive Decision Tree Induction on Dirty Data. 330 $aIn both the database and machine learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as ?dirty data.? Clearly, for a given data mining or machine learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of machine learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on machine learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers inthe database and machine learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of machine learning models; and cost-sensitive decision tree induction approaches under different scenarios. Further, the book opens many promising avenues for the further study of dirty data processing, such as data cleaning on demand, constructing a model to predict dirty-data impacts, and integrating data quality issues into other machine learning models. Readers will be introduced to state-of-the-art dirty data processing techniques, and the latest research advances, while also finding new inspirations in this field. 606 $aArtificial intelligence$xData processing 606 $aData mining 606 $aBig data 606 $aData Science 606 $aData Mining and Knowledge Discovery 606 $aBig Data 615 0$aArtificial intelligence$xData processing. 615 0$aData mining. 615 0$aBig data. 615 14$aData Science. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data. 676 $a005.7 700 $aQi$b Zhixin$01453413 701 $aWang$b Hongzhi$0654187 701 $aDong$b Zejiao$01453414 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768172903321 996 $aDirty Data Processing for Machine Learning$93656032 997 $aUNINA