Download or read book Decision Forests written by Antonio Criminisi and published by Foundations and Trends(r) in C. This book was released on 2012-03 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a unified, efficient model of random decision forests which can be used in a number of applications such as scene recognition from photographs, object recognition in images, automatic diagnosis from radiological scans and document analysis.

Download or read book Decision Forests for Computer Vision and Medical Image Analysis written by Antonio Criminisi and published by Springer Science & Business Media. This book was released on 2013-01-30 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision forests for such tasks as classification, regression, density estimation, manifold learning, active learning and semi-supervised classification; includes exercises and experiments throughout the text, with solutions, slides, demo videos and other supplementary material provided at an associated website; provides a free, user-friendly software library, enabling the reader to experiment with forests in a hands-on manner.

Download or read book Advanced Analytics with Spark written by Sandy Ryza and published by "O'Reilly Media, Inc.". This book was released on 2015-04-02 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder

Download or read book Encyclopedia of Mathematical Geosciences written by B. S. Daya Sagar and published by Springer Nature. This book was released on 2023-07-13 with total page 1744 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Mathematical Geosciences is a complete and authoritative reference work. It provides concise explanation on each term that is related to Mathematical Geosciences. Over 300 international scientists, each expert in their specialties, have written around 350 separate articles on different topics of mathematical geosciences including contributions on Artificial Intelligence, Big Data, Compositional Data Analysis, Geomathematics, Geostatistics, Geographical Information Science, Mathematical Morphology, Mathematical Petrology, Multifractals, Multiple Point Statistics, Spatial Data Science, Spatial Statistics, and Stochastic Process Modeling. Each topic incorporates cross-referencing to related articles, and also has its own reference list to lead the reader to essential articles within the published literature. The entries are arranged alphabetically, for easy access, and the subject and author indices are comprehensive and extensive.

Download or read book Random Forests with R written by Robin Genuer and published by Springer Nature. This book was released on 2020-09-10 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.

Download or read book Decision Support for Forest Management written by Annika Kangas and published by Springer. This book was released on 2015-10-27 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This updated and expanded second edition adds the most recent advances in participatory planning approaches and methods, giving special emphasis to decision support tools usable under uncertainty. The new edition places emphasis on the selection of criteria and creating alternatives in practical multi-criteria decision making problems.

Download or read book Decision Methods for Forest Resource Management written by Joseph Buongiorno and published by Academic Press. This book was released on 2003-02-06 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision Methods for Forest Resource Management focuses on decision making for forests that are managed for both ecological and economic objectives. The essential modern decision methods used in the scientific management of forests are described using basic algebra, computer spreadsheets, and numerous examples and applications. Balanced treatment is given throughout the book to the ecological and economic impacts of alternative management decisions in both even-aged and uneven-aged forests. In-depth coverage of both ecological and economic issues Hands-on examples with Excel spreadsheets; electronic versions available on the authors' website Many related exercises with solutions Instructor's Manual available upon request

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 Condition Monitoring with Vibration Signals written by Hosameldin Ahmed and published by John Wiley & Sons. This book was released on 2020-01-07 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an extensive, up-to-date treatment of techniques used for machine condition monitoring Clear and concise throughout, this accessible book is the first to be wholly devoted to the field of condition monitoring for rotating machines using vibration signals. It covers various feature extraction, feature selection, and classification methods as well as their applications to machine vibration datasets. It also presents new methods including machine learning and compressive sampling, which help to improve safety, reliability, and performance. Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines starts by introducing readers to Vibration Analysis Techniques and Machine Condition Monitoring (MCM). It then offers readers sections covering: Rotating Machine Condition Monitoring using Learning Algorithms; Classification Algorithms; and New Fault Diagnosis Frameworks designed for MCM. Readers will learn signal processing in the time-frequency domain, methods for linear subspace learning, and the basic principles of the learning method Artificial Neural Network (ANN). They will also discover recent trends of deep learning in the field of machine condition monitoring, new feature learning frameworks based on compressive sampling, subspace learning techniques for machine condition monitoring, and much more. Covers the fundamental as well as the state-of-the-art approaches to machine condition monitoringguiding readers from the basics of rotating machines to the generation of knowledge using vibration signals Provides new methods, including machine learning and compressive sampling, which offer significant improvements in accuracy with reduced computational costs Features learning algorithms that can be used for fault diagnosis and prognosis Includes previously and recently developed dimensionality reduction techniques and classification algorithms Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines is an excellent book for research students, postgraduate students, industrial practitioners, and researchers.

Download or read book Database and Expert Systems Applications written by Norman Revell and published by Springer Science & Business Media. This book was released on 2007-08-21 with total page 927 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 18th International Conference on Database and Expert Systems Applications held in September 2007. Papers are organized into topical sections covering XML, data and information, datamining and data warehouses, database applications, WWW, bioinformatics, process automation and workflow, knowledge management and expert systems, database theory, query processing, and privacy and security.

Download or read book Decision Tree and Ensemble Learning Based on Ant Colony Optimization written by Jan Kozak and published by Springer. This book was released on 2018-06-20 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.

Download or read book Biometry written by Ricardo A. Ramirez-Mendoza and published by CRC Press. This book was released on 2022-07-07 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biometrics provide quantitative representations of human features, physiological and behavioral. This book is a compilation of biometric technologies developed by various research groups in Tecnologico de Monterrey, Mexico. It provides a summary of biometric systems as a whole, explaining the principles behind physiological and behavioral biometrics and exploring different types of commercial and experimental technologies and current and future applications in the fields of security, military, criminology, healthcare education, business, and marketing. Examples of biometric systems using brain signals or electroencephalography (EEG) are given. Mobile and home EEG use in children’s natural environments is covered. At the same time, some examples focus on the relevance of such technology in monitoring epileptic encephalopathies in children. Using reliable physiological signal acquisition techniques, functional Human Machine Interfaces (HMI) and Brain-Computer Interfaces (BCI) become possible. This is the case of an HMI used for assistive navigation systems, controlled via voice commands, head, and eye movements. A detailed description of the BCI framework is presented, and applications of user-centered BCIs, oriented towards rehabilitation, human performance, and treatment monitoring are explored. Massive data acquisition also plays an essential role in the evolution of biometric systems. Machine learning, deep learning, and Artificial Intelligence (AI) are crucial allies here. They allow the construction of models that can aid in early diagnosis, seizure detection, and data-centered medical decisions. Such techniques will eventually lead to a more concise understanding of humans.

Download or read book Advancing the Investigation and Treatment of Sleep Disorders Using AI written by Kumar, M. Rajesh and published by IGI Global. This book was released on 2021-06-25 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are more than 80 different sleep disorders including insomnia, sleep apnea, restless leg syndrome, hypersomnia, circadian rhythm disorders, and parasomnia. Good sleep is necessary for optimal health and can affect hormone levels and weight. The use of artificial intelligence (AI) and biomedical signals and images can help in healthcare diagnostics that are related to these and other sleep disorders. Advancing the Investigation and Treatment of Sleep Disorders Using AI presents an overview of sleep disorders based on machine intelligence methods in order to learn and explore the latest advancements, developments, methods, systems, futuristic approaches, and algorithms towards sleep disorders and to address their challenges. This book also discusses recent and future advancements in various feature extraction techniques and machine learning methods. Covering topics such as biomedical signal processing, augmented reality for clinical investigation, and sleep disorder detection, this book is essential for sleep medicine practitioners, clinical psychologists, psychiatrists, medical technologists, doctors, IT specialists, biomedical engineers, researchers, graduate students, and academicians.

Download or read book Decision Trees and Random Forests written by Mark Koning and published by Independently Published. This book was released on 2017-10-04 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services. They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you.

Download or read book Machine Learning and Cybernetics written by Xizhao Wang and published by Springer. This book was released on 2014-12-04 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.

Download or read book Python Data Science Handbook written by Jake VanderPlas and published by "O'Reilly Media, Inc.". This book was released on 2016-11-21 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Download or read book QSAR in Safety Evaluation and Risk Assessment written by Huixiao Hong and published by Elsevier. This book was released on 2023-08-12 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: QSAR in Safety Evaluation and Risk Assessment provides comprehensive coverage on QSAR methods, tools, data sources, and models focusing on applications in products safety evaluation and chemicals risk assessment. Organized into five parts, the book covers almost all aspects of QSAR modeling and application. Topics in the book include methods of QSAR, from both scientific and regulatory viewpoints; data sources available for facilitating QSAR models development; software tools for QSAR development; and QSAR models developed for assisting safety evaluation and risk assessment. Chapter contributors are authored by a lineup of active scientists in this field. The chapters not only provide professional level technical summarizations but also cover introductory descriptions for all aspects of QSAR for safety evaluation and risk assessment. Provides comprehensive content about the QSAR techniques and models in facilitating the safety evaluation of drugs and consumer products and risk assesment of environmental chemicals Includes some of the most cutting-edge methodologies such as deep learning and machine learning for QSAR Offers detailed procedures of modeling and provides examples of each model's application in real practice