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 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 Intelligent Data Engineering and Automated Learning IDEAL 2004 written by Zhen Rong Yang and published by Springer Science & Business Media. This book was released on 2004-08-13 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, held in Exeter, UK, in August 2004. The 124 revised full papers presented were carefully reviewed and selected from 272 submissions. The papers are organized in topical sections on bioinformatics, data mining and knowledge engineering, learning algorithms and systems, financial engineering, and agent technologies.

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 Advanced Data Mining and Applications written by Longbing Cao and published by Springer. This book was released on 2010-11-18 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the ever-growing power of generating, transmitting, and collecting huge amounts of data, information overloadis nowan imminent problemto mankind. The overwhelming demand for information processing is not just about a better understanding of data, but also a better usage of data in a timely fashion. Data mining, or knowledge discovery from databases, is proposed to gain insight into aspects ofdata and to help peoplemakeinformed,sensible,and better decisions. At present, growing attention has been paid to the study, development, and application of data mining. As a result there is an urgent need for sophisticated techniques and toolsthat can handle new ?elds of data mining, e. g. , spatialdata mining, biomedical data mining, and mining on high-speed and time-variant data streams. The knowledge of data mining should also be expanded to new applications. The 6th International Conference on Advanced Data Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the world. It provided a leading international forum for the dissemination of original research results in advanced data mining techniques, applications, al- rithms, software and systems, and di?erent applied disciplines. The conference attracted 361 online submissions from 34 di?erent countries and areas. All full papers were peer reviewed by at least three members of the Program Comm- tee composed of international experts in data mining ?elds. A total number of 118 papers were accepted for the conference. Amongst them, 63 papers were selected as regular papers and 55 papers were selected as short papers.

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 374 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 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 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 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 324 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 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 Essentials written by Alboukadel Kassambara and published by STHDA. This book was released on 2018-03-10 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

Download or read book 2019 International Conference on Intelligent Computing and Control Systems ICCS written by IEEE Staff and published by . This book was released on 2019-05-15 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ICICCS 2019 will provide an outstanding international forum for scientists from all over the world to share ideas and achievements in the theory and practice of all areas of inventive systems which includes control, artificial intelligence, automation systems, computing systems, electronics systems, electrical and informative systems etc Presentations should highlight computing methodologies as a concept that combines theoretical research and applications in automation, information and computing technologies All aspects of intelligent computing and control systems are of interest theory, algorithms, tools, applications, etc

Download or read book Nearest neighbor Methods in Learning and Vision written by Gregory Shakhnarovich and published by . This book was released on 2005 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents theoretical and practical discussions of nearest neighbour (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic.

Download or read book Nearest Neighbor NN Norms written by Belur V. Dasarathy and published by . This book was released on 1991 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: