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Machine Methods
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Book Synopsis Automated Machine Learning by : Frank Hutter
Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Book Synopsis Machine Learning Methods for Stylometry by : Jacques Savoy
Download or read book Machine Learning Methods for Stylometry written by Jacques Savoy and published by Springer Nature. This book was released on 2020-09-28 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents methods and approaches used to identify the true author of a doubtful document or text excerpt. It provides a broad introduction to all text categorization problems (like authorship attribution, psychological traits of the author, detecting fake news, etc.) grounded in stylistic features. Specifically, machine learning models as valuable tools for verifying hypotheses or revealing significant patterns hidden in datasets are presented in detail. Stylometry is a multi-disciplinary field combining linguistics with both statistics and computer science. The content is divided into three parts. The first, which consists of the first three chapters, offers a general introduction to stylometry, its potential applications and limitations. Further, it introduces the ongoing example used to illustrate the concepts discussed throughout the remainder of the book. The four chapters of the second part are more devoted to computer science with a focus on machine learning models. Their main aim is to explain machine learning models for solving stylometric problems. Several general strategies used to identify, extract, select, and represent stylistic markers are explained. As deep learning represents an active field of research, information on neural network models and word embeddings applied to stylometry is provided, as well as a general introduction to the deep learning approach to solving stylometric questions. In turn, the third part illustrates the application of the previously discussed approaches in real cases: an authorship attribution problem, seeking to discover the secret hand behind the nom de plume Elena Ferrante, an Italian writer known worldwide for her My Brilliant Friend’s saga; author profiling in order to identify whether a set of tweets were generated by a bot or a human being and in this second case, whether it is a man or a woman; and an exploration of stylistic variations over time using US political speeches covering a period of ca. 230 years. A solutions-based approach is adopted throughout the book, and explanations are supported by examples written in R. To complement the main content and discussions on stylometric models and techniques, examples and datasets are freely available at the author’s Github website.
Book Synopsis Ensemble Methods for Machine Learning by : Gautam Kunapuli
Download or read book Ensemble Methods for Machine Learning written by Gautam Kunapuli and published by Simon and Schuster. This book was released on 2023-05-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: Methods for classification, regression, and recommendations Sophisticated off-the-shelf ensemble implementations Random forests, boosting, and gradient boosting Feature engineering and ensemble diversity Interpretability and explainability for ensemble methods Ensemble machine learning trains a diverse group of machine learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both data science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying ensemble machine learning to real-world data, and be ready to build more explainable ML systems. About the Technology Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble machine learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets. About the Book Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation! What’s Inside Bagging, boosting, and gradient boosting Methods for classification, regression, and retrieval Interpretability and explainability for ensemble methods Feature engineering and ensemble diversity About the Reader For Python programmers with machine learning experience. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles
Book Synopsis First-order and Stochastic Optimization Methods for Machine Learning by : Guanghui Lan
Download or read book First-order and Stochastic Optimization Methods for Machine Learning written by Guanghui Lan and published by Springer Nature. This book was released on 2020-05-15 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
Book Synopsis Hand and Machine Labor: General table - Production by hand and machine methods by : United States. Bureau of Labor
Download or read book Hand and Machine Labor: General table - Production by hand and machine methods written by United States. Bureau of Labor and published by . This book was released on 1899 with total page 1206 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Download or read book Ensemble Methods written by Zhi-Hua Zhou and published by CRC Press. This book was released on 2012-06-06 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
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.
Book Synopsis Variational Methods for Machine Learning with Applications to Deep Networks by : Lucas Pinheiro Cinelli
Download or read book Variational Methods for Machine Learning with Applications to Deep Networks written by Lucas Pinheiro Cinelli and published by Springer Nature. This book was released on 2021-05-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.
Book Synopsis Tentative Standard Methods of Sampling and Testing Highway Materials by : United States. Public Roads Administration
Download or read book Tentative Standard Methods of Sampling and Testing Highway Materials written by United States. Public Roads Administration and published by . This book was released on 1928 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by : Nello Cristianini
Download or read book An Introduction to Support Vector Machines and Other Kernel-based Learning Methods written by Nello Cristianini and published by Cambridge University Press. This book was released on 2000-03-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
Book Synopsis Machine Learning Methods in the Environmental Sciences by : William W. Hsieh
Download or read book Machine Learning Methods in the Environmental Sciences written by William W. Hsieh and published by Cambridge University Press. This book was released on 2009-07-30 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.
Book Synopsis Machine Learning Methods for Ecological Applications by : Alan H. Fielding
Download or read book Machine Learning Methods for Ecological Applications written by Alan H. Fielding and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem.
Book Synopsis Translating Between Hand and Machine Knitting by : Vikki Haffenden
Download or read book Translating Between Hand and Machine Knitting written by Vikki Haffenden and published by The Crowood Press. This book was released on 2018-08-17 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knitting is an enduringly popular and creative craft, but many associate the techniques primarily with hand knitting, believing machine knitting to require arcane knowledge. However, machine knitting is formed from the same stitch structures and is equally versatile. Translating between Hand and Machine Knitting rediscovers the potential of domestic machine knitting to open up new possibilities for dedicated knitters, offering an equally creative yet timesaving method of crafting new designs. With over 500 images, this comprehensive guide offers detailed step-by-step explanations of techniques in both hand and machine knitting, whilst also offering inspiration and design advice. Dr Vikki Haffenden, a highly experienced knit designer, technical knitter and educator, shares the knowledge and understanding she has accumulated over a career designing for the knitted textile and knitwear industry and as a lecturer in knitted textile design. Topics covered include: Illustrated step-by-step instructions of hand and machine knitting techniques; Qualities and behaviours of fibres and yarns and their suitability for knitting; Equipment requirements and advice; Basic stitches and stitch constructions of hand and machine knitting; Combining stitches to make surface texture and colour patterns; Shaping and knitting 3D shapes; Calculating garment shape and size from tension swatches. Superbly illsutrated with 397 colour photographs and 130 line artworks.
Book Synopsis Statistics and Machine Learning Methods for EHR Data by : Hulin Wu
Download or read book Statistics and Machine Learning Methods for EHR Data written by Hulin Wu and published by CRC Press. This book was released on 2020-12-09 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.
Book Synopsis Kernel Methods and Machine Learning by : S. Y. Kung
Download or read book Kernel Methods and Machine Learning written by S. Y. Kung and published by Cambridge University Press. This book was released on 2014-04-17 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
Book Synopsis Machine Learning Methods for Planning by : Steven Minton
Download or read book Machine Learning Methods for Planning written by Steven Minton and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning. Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credit assignment and describe tractable classes of problems for which optimal plans can be derived. This book discusses as well how reactive, integrated systems give rise to new requirements and opportunities for machine learning. The final chapter deals with a method for learning problem decompositions, which is based on an idealized model of efficiency for problem-reduction search. This book is a valuable resource for production managers, planners, scientists, and research workers.
