Lazy Learning

Author : David W. Aha
Publisher : Springer Science & Business Media
ISBN 13 : 9401720533
Total Pages : 421 pages
Book Rating : 4.33/5 ( download)

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Download or read book Lazy Learning written by David W. Aha and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited collection describes recent progress on lazy learning, a branch of machine learning concerning algorithms that defer the processing of their inputs, reply to information requests by combining stored data, and typically discard constructed replies. It is the first edited volume in AI on this topic, whose many synonyms include `instance-based', `memory-based'. `exemplar-based', and `local learning', and whose topic intersects case-based reasoning and edited k-nearest neighbor classifiers. It is intended for AI researchers and students interested in pursuing recent progress in this branch of machine learning, but, due to the breadth of its contributions, it should also interest researchers and practitioners of data mining, case-based reasoning, statistics, and pattern recognition.

Author : Lakhmi C. Jain
Publisher : Physica
ISBN 13 : 3790818038
Total Pages : 477 pages
Book Rating : 4.31/5 ( download)

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Download or read book New Learning Paradigms in Soft Computing written by Lakhmi C. Jain and published by Physica. This book was released on 2013-06-05 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.

Author : Maarten van Someren
Publisher : Springer Science & Business Media
ISBN 13 : 9783540628583
Total Pages : 380 pages
Book Rating : 4.84/5 ( download)

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Download or read book Machine Learning: ECML'97 written by Maarten van Someren and published by Springer Science & Business Media. This book was released on 1997-04-09 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Ninth European Conference on Machine Learning, ECML-97, held in Prague, Czech Republic, in April 1997. This volume presents 26 revised full papers selected from a total of 73 submissions. Also included are an abstract and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.

Author : Michael W. Berry
Publisher : SIAM
ISBN 13 : 9780898715682
Total Pages : 556 pages
Book Rating : 4.87/5 ( download)

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Download or read book Proceedings of the Fourth SIAM International Conference on Data Mining written by Michael W. Berry and published by SIAM. This book was released on 2004-01-01 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.

Author : Gonzalez, Fabio A.
Publisher : IGI Global
ISBN 13 : 1605669571
Total Pages : 390 pages
Book Rating : 4.71/5 ( download)

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Download or read book Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques written by Gonzalez, Fabio A. and published by IGI Global. This book was released on 2009-12-31 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical images are at the base of many routine clinical decisions and their influence continues to increase in many fields of medicine. Since the last decade, computers have become an invaluable tool for supporting medical image acquisition, processing, organization and analysis. Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques provides a panorama of the current boundary between biomedical complexity coming from the medical image context and the multiple techniques which have been used for solving many of these problems. This innovative publication serves as a leading industry reference as well as a source of creative ideas for applications of medical issues.

Author : Petra Perner
Publisher : Springer Science & Business Media
ISBN 13 : 3540734988
Total Pages : 927 pages
Book Rating : 4.87/5 ( download)

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Download or read book Machine Learning and Data Mining in Pattern Recognition written by Petra Perner and published by Springer Science & Business Media. This book was released on 2007-07-16 with total page 927 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ever wondered what the state of the art is in machine learning and data mining? Well, now you can find out. This book constitutes the refereed proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, held in Leipzig, Germany, in July 2007. The 66 revised full papers presented together with 1 invited talk were carefully reviewed and selected from more than 250 submissions. The papers are organized in topical sections.

Author : Ran Chen
Publisher : Springer Science & Business Media
ISBN 13 : 3642181287
Total Pages : 793 pages
Book Rating : 4.83/5 ( download)

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Download or read book Intelligent Computing and Information Science written by Ran Chen and published by Springer Science & Business Media. This book was released on 2010-12-23 with total page 793 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set (CCIS 134 and CCIS 135) constitutes the refereed proceedings of the International Conference on Intelligent Computing and Information Science, ICICIS2011, held in Chongqing, China, in January 2011. The 226 revised full papers presented in both volumes, CCIS 134 and CCIS 135, were carefully reviewed and selected from over 600 initial submissions. The papers provide the reader with a broad overview of the latest advances in the field of intelligent computing and information science.

Author : Claude Sammut
Publisher : Springer Science & Business Media
ISBN 13 : 0387307680
Total Pages : 1061 pages
Book Rating : 4.88/5 ( download)

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Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Author : Brett Lantz
Publisher : Packt Publishing Ltd
ISBN 13 : 1801076057
Total Pages : 763 pages
Book Rating : 4.50/5 ( download)

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Download or read book Machine Learning with R written by Brett Lantz and published by Packt Publishing Ltd. This book was released on 2023-05-29 with total page 763 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve real-world data problems using machine learning and R Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data. What you will learn Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Who this book is for This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.

Author : Gavin Hackeling
Publisher : Packt Publishing Ltd
ISBN 13 : 1788298497
Total Pages : 254 pages
Book Rating : 4.90/5 ( download)

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Download or read book Mastering Machine Learning with scikit-learn written by Gavin Hackeling and published by Packt Publishing Ltd. This book was released on 2017-07-24 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use scikit-learn to apply machine learning to real-world problems About This Book Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Who This Book Is For This book is intended for software engineers who want to understand how common machine learning algorithms work and develop an intuition for how to use them, and for data scientists who want to learn about the scikit-learn API. Familiarity with machine learning fundamentals and Python are helpful, but not required. What You Will Learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks In Detail Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. Style and approach This book is motivated by the belief that you do not understand something until you can describe it simply. Work through toy problems to develop your understanding of the learning algorithms and models, then apply your learnings to real-life problems.