Statistics

Statistical Learning & Data Mining

Per Page:
Sort:

Exploratory Data Analysis Using R

Ronald K. Pearson
May 04, 2018

Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" – good, bad, and ugly – features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to...

Sufficient Dimension Reduction: Methods and Applications with R

Bing Li
May 01, 2018

Sufficient dimension reduction is a rapidly developing research field that has wide applications in regression diagnostics, data visualization, machine learning, genomics, image processing, pattern recognition, and medicine, because they are fields that produce large datasets with a large number of...

Spectral Feature Selection for Data Mining

Zheng Alan Zhao, Huan Liu
April 18, 2018

Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified...

Feature Engineering for Machine Learning and Data Analytics

Guozhu Dong, Huan Liu
April 04, 2018

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the...

Data Stewardship for Open Science: Implementing FAIR Principles

Barend Mons
March 05, 2018

Data Stewardship for Open Science: Implementing FAIR Principles has been written with the intention of making scientists, funders, and innovators in all disciplines and stages of their professional activities broadly aware of the need, complexity, and challenges associated with open science, modern...

High Performance Computing for Big Data: Methodologies and Applications

Chao Wang
October 10, 2017

High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life...

Frontiers in Data Science

Matthias Dehmer, Frank Emmert-Streib
October 09, 2017

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation...

Big Data Analytics in Cybersecurity

Onur Savas, Julia Deng
September 20, 2017

Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging...

Introduction to Machine Learning with Applications in Information Security

Mark Stamp
September 07, 2017

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn’t prove theorems, or otherwise dwell on mathematical theory....

Statistical Regression and Classification: From Linear Models to Machine Learning

Norman Matloff
August 01, 2017

Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough...

Social Networks with Rich Edge Semantics

Quan Zheng, David Skillicorn
August 01, 2017

Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and...

The Essentials of Data Science: Knowledge Discovery Using R

Graham J. Williams
July 13, 2017

The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from...

Conferences

Year: