標(biāo)題: Titlebook: Data Analytics; Models and Algorithm Thomas A. Runkler Textbook 20121st edition Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden 2012 [打印本頁(yè)] 作者: Herbaceous 時(shí)間: 2025-3-21 19:30
書目名稱Data Analytics影響因子(影響力)
書目名稱Data Analytics影響因子(影響力)學(xué)科排名
書目名稱Data Analytics網(wǎng)絡(luò)公開(kāi)度
書目名稱Data Analytics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Data Analytics被引頻次
書目名稱Data Analytics被引頻次學(xué)科排名
書目名稱Data Analytics年度引用
書目名稱Data Analytics年度引用學(xué)科排名
書目名稱Data Analytics讀者反饋
書目名稱Data Analytics讀者反饋學(xué)科排名
作者: obeisance 時(shí)間: 2025-3-21 20:14
http://image.papertrans.cn/d/image/262675.jpg作者: Arthropathy 時(shí)間: 2025-3-22 02:15
https://doi.org/10.1007/978-0-387-88849-1, image data, and biomedical data. We define the terms data analytics, data mining, knowledge discovery, and the KDD and CRISP-DM processes. Typical data analysis projects can be divided into several phases: preparation, preprocessing, analysis, and postprocessing. The chapters of this book are stru作者: Binge-Drinking 時(shí)間: 2025-3-22 05:55
The Many Faces of the Single-Tuned Circuitnted for because certain mathematical operations are only appropriate for specific scales. Numerical data can be represented by sets, vectors, or matrices. Data analysis is often based on dissimilarity measures (like inner product norms, Lebesgue/Minkowski norms) or on similarity measures (like cosi作者: 一起平行 時(shí)間: 2025-3-22 11:29
Circuits, Systems and Signal Processing heterogeneous information sources.We distinguish deterministic and stochastic errors. Deterministic errors can sometimes be easily corrected. Outliers need to be identified and removed or corrected. Outliers or noise can be reduced by filtering. We distinguish many different filtering methods with 作者: Nutrient 時(shí)間: 2025-3-22 13:11
Basic Concepts in Signals and Systems. To visualize high-dimensional data, projection methods are necessary. We present linear projection (principal component analysis, Karhunen-Lo`eve transform, singular value decomposition, eigenvector projection, Hotelling transform, proper orthogonal decomposition) and nonlinear projection methods 作者: Nutrient 時(shí)間: 2025-3-22 20:30
The Circular Economy and Business Challengesinear correlation methods are robust and computationally efficient but detect only linear dependencies. Nonlinear correlationmethods are able to detect nonlinear dependencies but need to be carefully parametrized. As a popular example for nonlinear correlation we present the chi-square test for inde作者: plasma-cells 時(shí)間: 2025-3-23 00:02 作者: Rct393 時(shí)間: 2025-3-23 04:39
Sergei Yu. Venyaminov,Jen Tsi Yangy or a Moore machine. This leads to recurrent or auto-regressive models. Building forecasting models is essentially a regression task. The training data sets for forecasting models are generated by finite unfolding in time. Popular linear forecasting models are auto-regressive models (AR) and genera作者: 取回 時(shí)間: 2025-3-23 06:49
https://doi.org/10.1007/978-981-19-0549-0 define numerous indicators to quantify classifier performance. Pairs of indicators are considered to assess classification performance.We illustrate this with the receiver operating characteristic and the precision recall diagram. Several different classifiers with specific features and drawbacks a作者: 舊病復(fù)發(fā) 時(shí)間: 2025-3-23 11:02
Leadership for Sustainability in Crisis Timees, the clusters may or may not correspond with the physical classes. Cluster partitions may be mathematically represented by sets, partition matrices, and/or cluster prototypes. Sequential clustering (single linkage, complete linkage, average linkage, Ward’s method, etc.) is easily implemented but 作者: extemporaneous 時(shí)間: 2025-3-23 14:36 作者: Flawless 時(shí)間: 2025-3-23 18:30 作者: PLUMP 時(shí)間: 2025-3-24 00:25 作者: 孵卵器 時(shí)間: 2025-3-24 04:26
3D Print, Circularity, and Footprintson algorithms. In this appendix we briefly review the basics of the optimization methods used in this book: optimization with derivatives, gradient descent, and Lagrange optimization. For a more comprehensive overview of optimization methods the reader is referred to the literature, for example [1, 2, 3].作者: TERRA 時(shí)間: 2025-3-24 06:50
Brief Review of Some Optimization Methods,on algorithms. In this appendix we briefly review the basics of the optimization methods used in this book: optimization with derivatives, gradient descent, and Lagrange optimization. For a more comprehensive overview of optimization methods the reader is referred to the literature, for example [1, 2, 3].作者: 老巫婆 時(shí)間: 2025-3-24 10:55
rous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens.978-3-8348-2589-6作者: Evolve 時(shí)間: 2025-3-24 17:25 作者: 植物群 時(shí)間: 2025-3-24 21:46
Data and Relations,nted for because certain mathematical operations are only appropriate for specific scales. Numerical data can be represented by sets, vectors, or matrices. Data analysis is often based on dissimilarity measures (like inner product norms, Lebesgue/Minkowski norms) or on similarity measures (like cosi作者: artless 時(shí)間: 2025-3-25 01:59 作者: 過(guò)份 時(shí)間: 2025-3-25 03:53 作者: harmony 時(shí)間: 2025-3-25 10:54
Correlation,inear correlation methods are robust and computationally efficient but detect only linear dependencies. Nonlinear correlationmethods are able to detect nonlinear dependencies but need to be carefully parametrized. As a popular example for nonlinear correlation we present the chi-square test for inde作者: accrete 時(shí)間: 2025-3-25 11:52
Regression,ls can be efficiently computed from covariances but are restricted to linear dependencies. Substitution allows us to identify specific nonlinear dependencies by linear regression. Robust regression finds models that are robust against outliers. A popular family of nonlinear regression methods are un作者: 直覺(jué)沒(méi)有 時(shí)間: 2025-3-25 18:20
Forecasting,y or a Moore machine. This leads to recurrent or auto-regressive models. Building forecasting models is essentially a regression task. The training data sets for forecasting models are generated by finite unfolding in time. Popular linear forecasting models are auto-regressive models (AR) and genera作者: vector 時(shí)間: 2025-3-25 23:00
Classification, define numerous indicators to quantify classifier performance. Pairs of indicators are considered to assess classification performance.We illustrate this with the receiver operating characteristic and the precision recall diagram. Several different classifiers with specific features and drawbacks a作者: ROOF 時(shí)間: 2025-3-26 04:03 作者: 公理 時(shí)間: 2025-3-26 07:10 作者: blight 時(shí)間: 2025-3-26 09:20
Textbook 20121st editionzation, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed 作者: 搬運(yùn)工 時(shí)間: 2025-3-26 15:40 作者: AGGER 時(shí)間: 2025-3-26 18:34 作者: scoliosis 時(shí)間: 2025-3-26 23:29
Data Preprocessing,different effectiveness and computational complexities: moving statistical measures, discrete linear filters, finite impule response, infinite impulse response. Data features with different ranges often need to be standardized or transformed.作者: Enzyme 時(shí)間: 2025-3-27 04:33 作者: 座右銘 時(shí)間: 2025-3-27 05:24 作者: Incumbent 時(shí)間: 2025-3-27 11:38
Classification,re presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using regression trees.作者: 努力趕上 時(shí)間: 2025-3-27 15:18 作者: commensurate 時(shí)間: 2025-3-27 21:44
https://doi.org/10.1007/978-0-387-88849-1ctured according to the main methods of data preprocessing and data analysis: data and relations, data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering.作者: 調(diào)整 時(shí)間: 2025-3-28 01:38
The Many Faces of the Single-Tuned Circuitne, overlap, Dice, Jaccard, Tanimoto). Sequences can be analyzed using sequence relations (like Hamming, Levenshtein, edit distance). Data can be extracted from continuous signals by sampling and quantization. The Nyquist condition allows sampling without loss of information.作者: 大方一點(diǎn) 時(shí)間: 2025-3-28 03:27
Circuits, Systems and Signal Processingdifferent effectiveness and computational complexities: moving statistical measures, discrete linear filters, finite impule response, infinite impulse response. Data features with different ranges often need to be standardized or transformed.作者: Additive 時(shí)間: 2025-3-28 06:32
Basic Concepts in Signals and Systems(multidimensional scaling, Sammon mapping, auto-associator). Data distributions can be estimated and visualized using histogram techniques. Periodic time series can be analyzed and visualized using spectral analysis (cosine and sine transforms, amplitude and phase spectra).作者: Deference 時(shí)間: 2025-3-28 14:01 作者: 細(xì)微差別 時(shí)間: 2025-3-28 16:06
https://doi.org/10.1007/978-981-19-0549-0re presented in detail: the naive Bayes classifier, linear discriminant analysis, the support vector machine (SVM) using the kernel trick, nearest neighbor classifiers, learning vector quantification, and hierarchical classification using regression trees.作者: 一大塊 時(shí)間: 2025-3-28 22:02
The Circular Economy and Business Challengeseptron and radial basis function networks. Universal approximators can realize arbitrarily small training errors, but cross-validation is required to find models with low validation errors that generalize well on other data sets. Feature selection allows us to include only relevant features in regression models leading to more accurate models.作者: chassis 時(shí)間: 2025-3-28 23:03 作者: Junction 時(shí)間: 2025-3-29 03:47
sed for more than 10 years.Includes supplementary material: This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides
