Introduction to Algorithms for Data Mining and Machine Learning

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  • Publisher : Academic Press
  • Release : 15 July 2019
  • ISBN : 9780128172162
  • Page : 188 pages
  • Rating : 4.5/5 from 103 voters

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Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

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Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release Date : 2019-07-15
  • ISBN : 9780128172162
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but

Data Mining and Machine Learning

Data Mining and Machine Learning
  • Author : Mohammed J. Zaki,Wagner Meira, Jr
  • Publisher : Cambridge University Press
  • Release Date : 2020-01-31
  • ISBN : 9781108473989
GET THIS BOOKData Mining and Machine Learning

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Machine Learning and Data Mining

Machine Learning and Data Mining
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  • Publisher : Horwood Publishing
  • Release Date : 2007-05-14
  • ISBN : 1904275214
GET THIS BOOKMachine Learning and Data Mining

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. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as

Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release Date : 2019-06-17
  • ISBN : 9780128172179
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but

Data Mining and Analysis

Data Mining and Analysis
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  • Publisher : Cambridge University Press
  • Release Date : 2014-05-12
  • ISBN : 9780521766333
GET THIS BOOKData Mining and Analysis

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Metalearning

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  • Author : Pavel Brazdil,Christophe Giraud Carrier,Carlos Soares,Ricardo Vilalta
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-11-26
  • ISBN : 9783540732624
GET THIS BOOKMetalearning

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches

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A Concise Introduction to Machine Learning
  • Author : A.C. Faul
  • Publisher : CRC Press
  • Release Date : 2019-08-23
  • ISBN : 9781351204736
GET THIS BOOKA Concise Introduction to Machine Learning

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

Introduction to Machine Learning

Introduction to Machine Learning
  • Author : Ethem Alpaydin
  • Publisher : MIT Press
  • Release Date : 2014-08-29
  • ISBN : 9780262028189
GET THIS BOOKIntroduction to Machine Learning

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in

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  • Publisher : Elsevier
  • Release Date : 2011-02-03
  • ISBN : 9780080890364
GET THIS BOOKData Mining: Practical Machine Learning Tools and Techniques

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

Text Mining with Machine Learning

Text Mining with Machine Learning
  • Author : Jan Žižka,František Dařena,Arnošt Svoboda
  • Publisher : CRC Press
  • Release Date : 2019-11-20
  • ISBN : 9780429890260
GET THIS BOOKText Mining with Machine Learning

This book provides a perspective on the application of machine learning-based methods in knowledge discovery from natural languages texts. By analysing various data sets, conclusions which are not normally evident, emerge and can be used for various purposes and applications. The book provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms. The

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Understanding Machine Learning
  • Author : Shai Shalev-Shwartz,Shai Ben-David
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-19
  • ISBN : 9781107057135
GET THIS BOOKUnderstanding Machine Learning

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Machine Learning for Data Streams

Machine Learning for Data Streams
  • Author : Albert Bifet,Ricard Gavalda,Geoff Holmes,Bernhard Pfahringer
  • Publisher : MIT Press
  • Release Date : 2018-03-16
  • ISBN : 9780262346054
GET THIS BOOKMachine Learning for Data Streams

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining

An Introduction to Machine Learning

An Introduction to Machine Learning
  • Author : Miroslav Kubat
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • ISBN : 9783030819354
GET THIS BOOKAn Introduction to Machine Learning

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear

The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining
  • Author : Xindong Wu,Vipin Kumar
  • Publisher : CRC Press
  • Release Date : 2009-04-09
  • ISBN : 142008965X
GET THIS BOOKThe Top Ten Algorithms in Data Mining

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5,

Introduction to Data Mining

Introduction to Data Mining
  • Author : Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar
  • Publisher : Unknown
  • Release Date : 2018-04-13
  • ISBN : 0273769227
GET THIS BOOKIntroduction to Data Mining

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.