Download or read book Dimensionality Reduction with Unsupervised Nearest Neighbors written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2013-05-30 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.

Download or read book Data Mining in Agriculture written by Antonio Mucherino and published by Springer Science & Business Media. This book was released on 2009-09-22 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining in Agriculture represents a comprehensive effort to provide graduate students and researchers with an analytical text on data mining techniques applied to agriculture and environmental related fields. This book presents both theoretical and practical insights with a focus on presenting the context of each data mining technique rather intuitively with ample concrete examples represented graphically and with algorithms written in MATLAB®.

Download or read book Hands On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.

Download or read book Data Algorithms written by Mahmoud Parsian and published by "O'Reilly Media, Inc.". This book was released on 2015-07-13 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects. Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark. Topics include: Market basket analysis for a large set of transactions Data mining algorithms (K-means, KNN, and Naive Bayes) Using huge genomic data to sequence DNA and RNA Naive Bayes theorem and Markov chains for data and market prediction Recommendation algorithms and pairwise document similarity Linear regression, Cox regression, and Pearson correlation Allelic frequency and mining DNA Social network analysis (recommendation systems, counting triangles, sentiment analysis)

Download or read book Lectures on the Nearest Neighbor Method written by Gérard Biau and published by Springer. This book was released on 2015-12-08 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).

Download or read book On The Move to Meaningful Internet Systems 2003 CoopIS DOA and ODBASE written by Zahir Tari and published by Springer. This book was released on 2003-10-25 with total page 1565 pages. Available in PDF, EPUB and Kindle. Book excerpt: missions in fact also treat an envisaged mutual impact among them. As for the 2002 edition in Irvine, the organizers wanted to stimulate this cross-pollination with a program of shared famous keynote speakers (this year we got Sycara, - ble, Soley and Mylopoulos!), and encouraged multiple attendance by providing authors with free access to another conference or workshop of their choice. We received an even larger number of submissions than last year for the three conferences (360 in total) and the workshops (170 in total). Not only can we therefore again claim a measurable success in attracting a representative volume of scienti?c papers, but such a harvest allowed the program committees of course to compose a high-quality cross-section of worldwide research in the areas covered. In spite of the increased number of submissions, the Program Chairs of the three main conferences decided to accept only approximately the same number of papers for presentation and publication as in 2002 (i. e. , around 1 paper out of every 4–5 submitted). For the workshops, the acceptance rate was about 1 in 2. Also for this reason, we decided to separate the proceedings into two volumes with their own titles, and we are grateful to Springer-Verlag for their collaboration in producing these two books. The reviewing process by the respective program committees was very professional and each paper in the main conferences was reviewed by at least three referees.

Download or read book Advances in Databases and Information Systems written by Yannis Ioannidis and published by Springer Science & Business Media. This book was released on 2007-09-17 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th East European Conference on Advances in Databases and Information Systems, ADBIS 2007, held in Varna, Bulgaria, in September/October 2007. The 23 revised papers presented together with three invited lectures were carefully reviewed and selected from 77 submissions. The papers address current research on database theory, development of advanced DBMS technologies, and their advanced applications.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Machine Learning Algorithms From Scratch with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-11-16 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.

Download or read book Machine Learning for Hackers written by Drew Conway and published by "O'Reilly Media, Inc.". This book was released on 2012-02-13 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data

Download or read book K Nearest Neighbor Algorithm written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-23 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is K Nearest Neighbor Algorithm The k-nearest neighbors technique, also known as k-NN, is a non-parametric supervised learning method that was initially created in 1951 by Evelyn Fix and Joseph Hodges in the field of statistics. Thomas Cover later expanded on the original concept. It has applications in both regression and classification. In both scenarios, the input is made up of the k training instances in a data collection that are the closest to one another. Whether or not k-NN was used for classification or regression, the results are as follows:The output of a k-nearest neighbor classification is a class membership. A plurality of an item's neighbors votes on how the object should be classified, and the object is then assigned to the class that is most popular among its k nearest neighbors (where k is a positive number that is often quite small). If k is equal to one, then the object is simply classified as belonging to the category of its single closest neighbor.The result of a k-NN regression is the value of a certain property associated with an object. This value is the average of the values of the k neighbors that are the closest to the current location. If k is equal to one, then the value of the output is simply taken from the value of the one nearest neighbor. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: K-nearest neighbors algorithm Chapter 2: Supervised learning Chapter 3: Pattern recognition Chapter 4: Curse of dimensionality Chapter 5: Nearest neighbor search Chapter 6: Cluster analysis Chapter 7: Kernel method Chapter 8: Large margin nearest neighbor Chapter 9: Structured kNN Chapter 10: Weak supervision (II) Answering the public top questions about k nearest neighbor algorithm. (III) Real world examples for the usage of k nearest neighbor algorithm in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of k nearest neighbor algorithm' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of k nearest neighbor algorithm.

Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Download or read book Ontology Based Information Retrieval for Healthcare Systems written by Vishal Jain and published by John Wiley & Sons. This book was released on 2020-07-29 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advancements of semantic web, ontology has become the crucial mechanism for representing concepts in various domains. For research and dispersal of customized healthcare services, a major challenge is to efficiently retrieve and analyze individual patient data from a large volume of heterogeneous data over a long time span. This requirement demands effective ontology-based information retrieval approaches for clinical information systems so that the pertinent information can be mined from large amount of distributed data. This unique and groundbreaking book highlights the key advances in ontology-based information retrieval techniques being applied in the healthcare domain and covers the following areas: Semantic data integration in e-health care systems Keyword-based medical information retrieval Ontology-based query retrieval support for e-health implementation Ontologies as a database management system technology for medical information retrieval Information integration using contextual knowledge and ontology merging Collaborative ontology-based information indexing and retrieval in health informatics An ontology-based text mining framework for vulnerability assessment in health and social care An ontology-based multi-agent system for matchmaking patient healthcare monitoring A multi-agent system for querying heterogeneous data sources with ontologies for reducing cost of customized healthcare systems A methodology for ontology based multi agent systems development Ontology based systems for clinical systems: validity, ethics and regulation

Download or read book Foundations of Multidimensional and Metric Data Structures written by Hanan Samet and published by Morgan Kaufmann. This book was released on 2006-08-08 with total page 1023 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher Description

Download or read book Smart Intelligent Computing and Applications written by Suresh Chandra Satapathy and published by Springer. This book was released on 2018-10-01 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings covers advanced and multi-disciplinary research on design of smart computing and informatics. The theme of the book broadly focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries. The volume publishes quality work pertaining to the scope of the conference which is extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare.

Download or read book Explaining the Success of Nearest Neighbor Methods in Prediction written by George H. Chen and published by Foundations and Trends (R) in Machine Learning. This book was released on 2018-05-30 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the success of Nearest Neighbor Methods in Prediction, both in theory and in practice.

Download or read book Discovering Knowledge in Data written by Daniel T. Larose and published by John Wiley & Sons. This book was released on 2005-01-28 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.