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Books in Machine learning

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41-50 of 54 results in All results

Artificial Intelligence V

  • 1st Edition
  • June 28, 2014
  • B. du Boulay + 1 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 7 7 9 - 8
Recent results and ongoing research in Artificial Intelligence are described in this book, with emphasis on fundamental questions in several key areas: machine learning, neural networks, automated reasoning, natural language processing, and logic methods in AI. There are also more applied papers in the fields of vision, architectures for KBS, expert systems and intelligent tutoring systems. One of the changes since AIMSA'90 has been the increased numbers of papers submitted in the fields of machine learning, neural networks and hybrid systems.One of the special features of the AIMSA series of conferences is their coverage of work across both Eastern and Western Europe and the former Soviet Union as well as papers from North America. AIMSA'92 is no exception and this volume provides a unique multi-cultural view of AI.

Data Mining: Concepts and Techniques

  • 3rd Edition
  • June 9, 2011
  • Jiawei Han + 2 more
  • English
  • eBook
    9 7 8 - 0 - 1 2 - 3 8 1 4 8 0 - 7
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

Data Mining

  • 3rd Edition
  • February 3, 2011
  • Ian H. Witten + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 9 0 3 6 - 4
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

Data Preparation for Data Mining Using SAS

  • 1st Edition
  • July 27, 2010
  • Mamdouh Refaat
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 1 0 0 - 4
Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little “how to” information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.

Data Mining: Know It All

  • 1st Edition
  • October 31, 2008
  • Soumen Chakrabarti + 14 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 7 4 6 2 9 - 0
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 7 7 8 8 - 4
This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.

Machine Learning and Data Mining

  • 1st Edition
  • April 30, 2007
  • Igor Kononenko + 1 more
  • English
  • Paperback
    9 7 8 - 1 - 9 0 4 2 7 5 - 2 1 - 3
  • eBook
    9 7 8 - 0 - 8 5 7 0 9 - 9 4 4 - 0
Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.

Java Data Mining: Strategy, Standard, and Practice

  • 1st Edition
  • November 7, 2006
  • Mark F. Hornick + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 9 5 9 1 - 0
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard.

Data Mining

  • 2nd Edition
  • July 13, 2005
  • Ian H. Witten + 1 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 4 7 7 0 2 - 2
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.

Categorization by Humans and Machines

  • 1st Edition
  • September 16, 1993
  • Glenn V. Nakamura + 2 more
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 6 3 8 0 - 1
The objective of the series has always been to provide a forum in which leading contributors to an area can write about significant bodies of research in which they are involved. The operating procedure has been to invite contributions from interesting, active investigators, and then allow them essentially free rein to present their perspectives on important research problems. The result of such invitations over the past two decades has been collections of papers which consist of thoughtful integrations providing an overview of a particular scientific problem. The series has an excellent tradition of high quality papers and is widely read by researchers in cognitive and experimental psychology.

Pattern Recognition and Machine Learning

  • 1st Edition
  • July 14, 1992
  • Y. Anzai
  • English
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 1 3 6 3 - 8
This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.