Introduction to Algorithms for Data Mining and Machine Learning Book

Introduction to Algorithms for Data Mining and Machine Learning


  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release Date : 2019-06-17
  • Genre: Mathematics
  • Pages : 188
  • ISBN 10 : 9780128172179

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Introduction to Algorithms for Data Mining and Machine Learning Excerpt :

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

Machine Learning and Data Mining Book
Score: 3
From 1 Ratings

Machine Learning and Data Mining


  • Author : Igor Kononenko
  • Publisher : Horwood Publishing
  • Release Date : 2007-05-14
  • Genre: Computers
  • Pages : 454
  • ISBN 10 : 1904275214

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Machine Learning and Data Mining Excerpt :

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 researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Data Mining and Machine Learning Book

Data Mining and Machine Learning


  • Author : Mohammed J. Zaki
  • Publisher : Cambridge University Press
  • Release Date : 2020-01-31
  • Genre: Business & Economics
  • Pages : 775
  • ISBN 10 : 9781108473989

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Data Mining and Machine Learning Excerpt :

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

Introduction to Algorithms for Data Mining and Machine Learning Book

Introduction to Algorithms for Data Mining and Machine Learning


  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release Date : 2019-07-15
  • Genre: Mathematics
  • Pages : 188
  • ISBN 10 : 9780128172162

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Introduction to Algorithms for Data Mining and Machine Learning Excerpt :

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

Data Mining and Analysis Book

Data Mining and Analysis


  • Author : Mohammed J. Zaki
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-12
  • Genre: Computers
  • Pages : 562
  • ISBN 10 : 9780521766333

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Data Mining and Analysis Excerpt :

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

Metalearning Book

Metalearning


  • Author : Pavel Brazdil
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-11-26
  • Genre: Computers
  • Pages : 176
  • ISBN 10 : 9783540732624

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Metalearning Excerpt :

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 to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

A Concise Introduction to Machine Learning Book

A Concise Introduction to Machine Learning


  • Author : A.C. Faul
  • Publisher : CRC Press
  • Release Date : 2019-08-23
  • Genre: Business & Economics
  • Pages : 314
  • ISBN 10 : 9781351204736

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A Concise Introduction to Machine Learning Excerpt :

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 Book
Score: 3
From 2 Ratings

Introduction to Machine Learning


  • Author : Ethem Alpaydin
  • Publisher : MIT Press
  • Release Date : 2014-08-29
  • Genre: Computers
  • Pages : 640
  • ISBN 10 : 9780262028189

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Introduction to Machine Learning Excerpt :

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 introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

Data Mining  Practical Machine Learning Tools and Techniques Book
Score: 5
From 2 Ratings

Data Mining Practical Machine Learning Tools and Techniques


  • Author : Ian H. Witten
  • Publisher : Elsevier
  • Release Date : 2011-02-03
  • Genre: Computers
  • Pages : 664
  • ISBN 10 : 9780080890364

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Data Mining Practical Machine Learning Tools and Techniques Excerpt :

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. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classif

Text Mining with Machine Learning Book

Text Mining with Machine Learning


  • Author : Jan Žižka
  • Publisher : CRC Press
  • Release Date : 2019-11-20
  • Genre: Computers
  • Pages : 352
  • ISBN 10 : 9780429890260

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Text Mining with Machine Learning Excerpt :

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 book is not only aimed at IT specialists, but is meant for a wider audience that needs to process big sets of text documents and has basic knowledge of the subject, e.g. e-mail service providers, online shoppers, librarians, etc. The book starts with an introduction to text-based natural language data processing and its goals and problems. It focuses on machine learning, presenting various algorithms with their use and possibilities, and reviews the positives and negatives. Beginning with the initial data pre-processing, a reader can follow the steps provided in the R-language including the subsuming of various available plug-ins into the resulting software tool. A big advantage is that R also contains many libraries implementing machine learning algorithms, so a reader can concentrate on the principal target without the need to implement the details of the algorithms her- or himself. To make sense of the results, the book also provides explanations of the algorithms, which supports the final evaluation and interpretation of the results. The examples are demonstrated using realworld data from commonly accessible Internet sources.

Understanding Machine Learning Book
Score: 5
From 2 Ratings

Understanding Machine Learning


  • Author : Shai Shalev-Shwartz
  • Publisher : Cambridge University Press
  • Release Date : 2014-05-19
  • Genre: Computers
  • Pages : 409
  • ISBN 10 : 9781107057135

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Understanding Machine Learning Excerpt :

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 Book

Machine Learning for Data Streams


  • Author : Albert Bifet
  • Publisher : MIT Press
  • Release Date : 2018-03-16
  • Genre: Computers
  • Pages : 288
  • ISBN 10 : 9780262346054

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Machine Learning for Data Streams Excerpt :

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 and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Introduction to Data Mining Book
Score: 5
From 1 Ratings

Introduction to Data Mining


  • Author : Pang-Ning Tan
  • Publisher : Unknown
  • Release Date : 2018-04-13
  • Genre: Data mining
  • Pages : 864
  • ISBN 10 : 0273769227

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Introduction to Data Mining Excerpt :

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.

The Top Ten Algorithms in Data Mining Book
Score: 4
From 1 Ratings

The Top Ten Algorithms in Data Mining


  • Author : Xindong Wu
  • Publisher : CRC Press
  • Release Date : 2009-04-09
  • Genre: Computers
  • Pages : 208
  • ISBN 10 : 142008965X

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The Top Ten Algorithms in Data Mining Excerpt :

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, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

An Introduction to Machine Learning Book

An Introduction to Machine Learning


  • Author : Miroslav Kubat
  • Publisher : Springer Nature
  • Release Date : 2021-09-25
  • Genre: Computers
  • Pages : 458
  • ISBN 10 : 9783030819354

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An Introduction to Machine Learning Excerpt :

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 and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.