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Data science and predictive analytic...

Dinov, Ivo D.

Data science and predictive analyticsbiomedical and health applications using R /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science and predictive analyticsby Ivo D. Dinov.
其他題名:	biomedical and health applications using R /
作者:	Dinov, Ivo D.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xxxiv, 832 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Big data.
電子資源:	https://doi.org/10.1007/978-3-319-72347-1
ISBN:	9783319723471$q(electronic bk.)

Data science and predictive analyticsbiomedical and health applications using R /
Dinov, Ivo D.

Data science and predictive analyticsbiomedical and health applications using R /[electronic resource] :by Ivo D. Dinov. - Cham :Springer International Publishing :2018. - xxxiv, 832 p. :ill., digital ;24 cm.

1 Introduction -- 2 Foundations of R -- 3 Managing Data in R -- 4 Data Visualization -- 5 Linear Algebra & Matrix Computing -- 6 Dimensionality Reduction -- 7 Lazy Learning: Classification Using Nearest Neighbors -- 8 Probabilistic Learning: Classification Using Naive Bayes -- 9 Decision Tree Divide and Conquer Classification -- 10 Forecasting Numeric Data Using Regression Models -- 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines -- 12 Apriori Association Rules Learning -- 13 k-Means Clustering -- 14 Model Performance Assessment -- 15 Improving Model Performance -- 16 Specialized Machine Learning Topics -- 17 Variable/Feature Selection -- 18 Regularized Linear Modeling and Controlled Variable Selection -- 19 Big Longitudinal Data Analysis -- 20 Natural Language Processing/Text Mining -- 21 Prediction and Internal Statistical Cross Validation -- 22 Function Optimization -- 23 Deep Learning Neural Networks -- 24 Summary -- 25 Glossary -- 26 Index -- 27 Errata.

ISBN: 9783319723471$q(electronic bk.)

Standard No.: 10.1007/978-3-319-72347-1doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45 / D56 2018

Dewey Class. No.: 005.7

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505 0 $a 1 Introduction -- 2 Foundations of R -- 3 Managing Data in R -- 4 Data Visualization -- 5 Linear Algebra & Matrix Computing -- 6 Dimensionality Reduction -- 7 Lazy Learning: Classification Using Nearest Neighbors -- 8 Probabilistic Learning: Classification Using Naive Bayes -- 9 Decision Tree Divide and Conquer Classification -- 10 Forecasting Numeric Data Using Regression Models -- 11 Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines -- 12 Apriori Association Rules Learning -- 13 k-Means Clustering -- 14 Model Performance Assessment -- 15 Improving Model Performance -- 16 Specialized Machine Learning Topics -- 17 Variable/Feature Selection -- 18 Regularized Linear Modeling and Controlled Variable Selection -- 19 Big Longitudinal Data Analysis -- 20 Natural Language Processing/Text Mining -- 21 Prediction and Internal Statistical Cross Validation -- 22 Function Optimization -- 23 Deep Learning Neural Networks -- 24 Summary -- 25 Glossary -- 26 Index -- 27 Errata.
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