Learn IT - Books tags discovery

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Springer, 2019

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Advanced Microservices: A Hands-on Approach to Microservice Infrastructure and Tooling

Apress, 2017

Use the many types of tools required to navigate and maintain a microservice ecosystem. This book examines what is normally a complex system of interconnected services and clarifies them one at a time, first examining theoretical requirements then looking at concrete tools, configuration, and workflows.

Building out these systems...

Algorithms in Combinatorial Design Theory (Mathematics Studies)

Elsevier Limited, 1985

Recent years have seen an explosive growth in research in combinatorics and graph theory. One primary factor in this rapid development has been the advent of computers, and the parallel study of practical and efficient algorithms. This volume represents an attempt to...

Social and Political Implications of Data Mining: Knowledge Management in E-Government

Information Science Publishing, 2008

In recent years, data mining has become a powerful tool in assisting society with its various layers and individual elements useful in obtaining intelligent information for making knowledgeable decisions. In the realm of knowledge discovery, data mining is becoming one of the most popular topics in information technology.

Social...

Data Mining and Knowledge Discovery for Big Data: Methodologies, Challenge and Opportunities (Studies in Big Data)

Springer, 2013

The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for...

Inference Control in Statistical Databases: From Theory to Practice (Lecture Notes in Computer Science)

Springer, 2002

Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source...

Genetic Programming Theory and Practice X (Genetic and Evolutionary Computation)

Springer, 2013

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP.

Topics in this volume include: evolutionary constraints,...

Domain Driven Data Mining

Springer, 2010

In the present thriving global economy a need has evolved for complex data analysis to enhance an organization’s production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and...

Native Docker Clustering with Swarm

Packt Publishing, 2017

Key Features

Get to grips with Docker Swarm, one of the key components of the Docker ecosystem.

Optimize Swarm and SwarmKit features for scaling massive applications through containers.

Learn about Docker’s scheduling tricks, high availability, security, and platform scalability.

...

Bayesian Artificial Intelligence, Second Edition

CRC Press, 2010

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian...

Data Mining: Concepts, Models and Techniques (Intelligent Systems Reference Library)

Springer, 2011

Data Mining represents a complex of technologies that are rooted in many disciplines: mathematics, statistics, computer science, physics, engineering, biology, etc., and with diverse applications in a large variety of different domains: business, health care, science and engineering, etc. Basically, data mining can be seen as the science of...

Using Docker: Developing and Deploying Software with Containers

O'Reilly, 2016

Docker containers offer simpler, faster, and more robust methods for developing, distributing, and running software than previously available. With this hands-on guide, you’ll learn why containers are so important, what you’ll gain by adopting Docker, and how to make it part of your development...

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