Introduction to Algorithms for Data Mining and Machine Learning

Written By Xin-She Yang
Introduction to Algorithms for Data Mining and Machine Learning
  • Publsiher : Academic Press
  • Release : 15 July 2019
  • ISBN : 0128172169
  • Pages : 188 pages
  • Rating : /5 from reviews
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Download or read book entitled Introduction to Algorithms for Data Mining and Machine Learning by author: Xin-She Yang which was release on 15 July 2019 and published by Academic Press with total page 188 pages . This book available in PDF, EPUB and Kindle Format. 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

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
  • Total pages : 188
  • ISBN : 0128172169
GET BOOK

Summary : 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 ...

Machine Learning and Data Mining

Machine Learning and Data Mining
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  • Publisher : Elsevier
  • Release Date : 2007-04-30
  • Total pages : 480
  • ISBN : 0128172169
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Summary : 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 ...

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  • Publisher : Univ of California Press
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  • ISBN : 0128172169
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Summary : "We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data ...

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  • Publisher : Addison-Wesley
  • Release Date : 2019
  • Total pages : 839
  • ISBN : 0128172169
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Summary : Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course. It is also suitable for individuals seeking an introduction to data mining. The text assumes only a modest statistics or mathematics background, and no database knowledge is needed. Introduction to Data Mining presents fundamental concepts ...

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  • Publisher : Morgan Kaufmann
  • Release Date : 2016-10-01
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  • ISBN : 0128172169
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Summary : Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches ...

Introduction to Machine Learning

Introduction to Machine Learning
  • Author : Ethem Alpaydin
  • Publisher : MIT Press
  • Release Date : 2004
  • Total pages : 415
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Summary : An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining....

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data Mining and Knowledge Discovery with Evolutionary Algorithms
  • Author : Alex A. Freitas
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-11-11
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  • ISBN : 0128172169
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Summary : This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an active research area. In general, data mining consists of extracting knowledge from data. The motivation for applying evolutionary ...

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  • Publisher : MIT Press
  • Release Date : 2001
  • Total pages : 546
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Summary : Measuremente and Data. Visualizing and Exploring Data. Data Analysis and Uncertainty. A Systematic Overview of Data Mining Algorithms. Models and Patterns. Score Functions for Data Mining Algorithms. Serach and Optimization Methods. Descriptive Modeling. Predictive Modeling for Classification. Predictive Modeling for Regression. Data Organization and Databases. Finding Patterns and Rules. Retrieval ...

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  • Publisher : MIT Press
  • Release Date : 2009-12-04
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Summary : A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions. 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, ...

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  • Publisher : BoD – Books on Demand
  • Release Date : 2020-03-25
  • Total pages : 232
  • ISBN : 0128172169
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Summary : Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four ...

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  • Author : A.C. Faul
  • Publisher : CRC Press
  • Release Date : 2019-08-01
  • Total pages : 314
  • ISBN : 0128172169
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Summary : 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 ...

Introduction to Data Mining and its Applications

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  • Publisher : Springer
  • Release Date : 2006-10-12
  • Total pages : 828
  • ISBN : 0128172169
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Summary : This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural ...

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  • Author : Masashi Sugiyama
  • Publisher : Morgan Kaufmann
  • Release Date : 2015-10-31
  • Total pages : 534
  • ISBN : 0128172169
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Summary : Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as ...

Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics
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  • Publisher : CRC Press
  • Release Date : 2008-06-05
  • Total pages : 384
  • ISBN : 0128172169
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Summary : Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical ...

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  • Publisher : Springer Nature
  • Release Date : 2019-11-11
  • Total pages : 286
  • ISBN : 0128172169
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Summary : This book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural ...