Titlebook: Mathematical Problems in Data Science; Theoretical and Prac Li M. Chen,Zhixun Su,Bo Jiang Book 2015 Springer International Publishing Switz

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Machine Learning for Data Science: Mathematical or Computationalal data sets. When more data samples are available, the algorithm must be able to adjust accordingly. Therefore, in cloud computing, and BigData related methods in data science, machine learning becomes the primary technology. We have introduced the PCA, .-NN and .-means, and other methods in artifi

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Images, Videos, and BigDatansider massive data processing. For instance, automated driving is a challenge to data science..In BigData related image processing, we will discuss the following topics in this chapter: (1) An overview of image and video segmentation, (2) Data storage and fast image segmentation, (3) Feature extrac

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A New Computational Model of Bigdata between the slave nodes is considered impossible or too costly, and (3) an extra slave processor, together with the data it carries, can be easily integrated into the system to support scalability. Under such a model capturing the most important characteristics of a practical MapReduce system, some

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Book 2015ree parts.  The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematic

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Li M. Chenals: 2nd Workshop on Knowledge Graphs Analysis on a Large Scale, MADEISD: 5th Workshop on Modern Approaches in Data Engineering, Information System Design, PeRS978-3-031-42940-8978-3-031-42941-5Series ISSN 1865-0929 Series E-ISSN 1865-0937

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ic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematic978-3-319-79739-7978-3-319-25127-1

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Overview of Basic Methods for Data Science graph search algorithms, statistical methods especially principal component analysis (PCA), algorithms and data structures, and data mining and pattern recognition. This chapter will provide an overview for machine learning in relation to other mathematical tools. We will first introduce graphs and

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