Learn IT - Books tags clustering

Introducing Windows Server 2008

Microsoft Press, 2007

Your first look at the next generation of Windows Server-straight from the experts.

Get a jump on evaluating Windows Server 2008-with technical insights direct from the Windows Server team. This practical introduction delivers real-world implementation scenarios and pragmatic advice for administering Windows Server in the enterprise.

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Temporal Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery)

CRC Press, 2010

Temporal data mining deals with the harvesting of useful information from temporal data. New initiatives in health care and business organizations have increased the importance of temporal information in data today.

From basic data mining concepts to state-of-the-art advances, Temporal Data Mining covers...

Hesitant Fuzzy Sets Theory (Studies in Fuzziness and Soft Computing)

Springer, 2014

This book provides the readers with a thorough and systematic introduction to hesitant fuzzy theory. It presents the most recent research results and advanced methods in the field. These includes: hesitant fuzzy aggregation techniques, hesitant fuzzy preference relations, hesitant fuzzy measures, hesitant fuzzy clustering algorithms and...

Recent Advances in Intelligent Informatics: Proceedings of the Second International Symposium on Intelligent Informatics (ISI'13), August 23-24 2013, ... in Intelligent Systems and Computing)

Springer, 2013

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Symposium on Intelligent Informatics (ISI 2013) held in Mysore, India during August 23-24, 2013. The 47 revised papers presented were carefully reviewed and selected from 126 initial submissions. The papers are organized in topical sections...

Graph -Theoretic Techniques for Web Content Mining (Machine Perception and Artificial Intelligence)

World Scientific Publishing, 2005

Graphs are mathematical constructs for representing objects or systems which contain structural (i.e. relationship) information. Graphs have been used in many domains, from software engineering to artificial intelligence. However, the use of graphs in machine learning has been limited, compared to the more prevalent vector model which does not...

Analyzing Microarray Gene Expression Data (Wiley Series in Probability and Statistics)

John Wiley & Sons, 2004

A multi-discipline, hands-on guide to microarray analysis of biological processes

Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis...

Optimizing Hadoop for MapReduce

Packt Publishing, 2014

This book is the perfect introduction to sophisticated concepts in MapReduce and will ensure you have the knowledge to optimize job performance. This is not an academic treatise; it's an example-driven tutorial for the real world.

Overview

Optimize your MapReduce job performance

Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)

Springer, 2005

‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph,...

Machine Learning with Spark - Tackle Big Data with Powerful Spark Machine Learning Algorithms

Packt Publishing, 2014

Key Features

Follow real-world examples to learn how to develop your own machine learning systems with Spark

A practical tutorial with real-world use cases allowing you to develop your own machine learning systems with Spark

Combine various techniques and models into an intelligent machine...

Advanced Analytics with Spark: Patterns for Learning from Data at Scale

O'Reilly, 2015

In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.

You’ll start with an...

Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition)

Addison Wesley, 2004

Can machines think like people? This question is the driving force behind Artificial Intelligence, but it is only the starting point of this ever-evolving, exciting discipline. AI uses different strategies to solve the complex problems that arise wherever computer technology is applied, from those areas pertaining to perception and adaptation...

Machine Learning with R

Packt Publishing, 2013

Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications

Overview

Harness the power of R for statistical computing and data science

Use R to apply common machine learning algorithms with real-world applications

Prepare,...

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