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Soft Methods for Data Science

  • Conference proceedings
  • © 2017

Overview

  • Latest research on Data Analysis and Soft Computing
  • Results of the 8th International Conference on Soft Methods in Probability and Statistics (SMPS'2016) held in Rome (Italy) by September 12-14, 2016
  • Gives a comprehensive overview of current research into the fusion of soft computing methods with probability and statistics
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Intelligent Systems and Computing (AISC, volume 456)

Included in the following conference series:

Conference proceedings info: SMPS 2016.

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Table of contents (65 papers)

Other volumes

  1. Soft Methods for Data Science

Keywords

About this book

This proceedings volume is a collection of peer reviewed papers presented at the 8th International Conference on Soft Methods in Probability and Statistics (SMPS 2016) held in Rome (Italy).

The book is dedicated to Data science which aims at developing automated methods to analyze massive amounts of data and to extract knowledge from them. It shows how Data science employs various programming techniques and methods of data wrangling, data visualization, machine learning, probability and statistics. The soft methods proposed in this volume represent a collection of tools in these fields that can also be useful for data science.

Editors and Affiliations

  • Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maria Brigida Ferraro

  • Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy

    Paolo Giordani

  • Dept of Basic & Applied Sciences Engg, Sapienza University of Rome, Rome, Italy

    Barbara Vantaggi

  • Dept of Stoc Metds,Polish Academy of Sci, Systems Research Institute, Warsaw, Poland

    Marek Gagolewski

  • Dept of Statis&OR and MD, Univ de Oviedo, Oviedo, Spain

    María Ángeles Gil

  • Dept of Stoc Metds,Polish Aca of Science, Systems Res Inst, Warsaw, Poland

    Przemysław Grzegorzewski

  • Sys Res Inti, Dept of Stoch Methods, Polish Acadmy Sci in Warsaw, Warsaw, Poland

    Olgierd Hryniewicz

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