Boosting

Author: Robert E. Schapire
Publisher: MIT Press
ISBN: 0262017180
Size: 16.49 MB
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Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate "rules of thumb." A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical. This book, written by the inventors of the method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds while also providing an authoritative reference for advanced researchers. With its introductory treatment of all material and its inclusion of exercises in every chapter, the book is appropriate for course use as well. The book begins with a general introduction to machine learning algorithms and their analysis; then explores the core theory of boosting, especially its ability to generalize; examines some of the myriad other theoretical viewpoints that help to explain and understand boosting; provides practical extensions of boosting for more complex learning problems; and finally presents a number of advanced theoretical topics. Numerous applications and practical illustrations are offered throughout.

Big Data Bigdata 2018

Author: Francis Y. L. Chin
Publisher: Springer
ISBN: 3319943014
Size: 22.92 MB
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This volume constitutes the proceedings of the 7th International Conference on BIGDATA 2018, held as Part of SCF 2018 in Seattle, WA, USA in June 2018. The 22 full papers together with 10 short papers published in this volume were carefully reviewed and selected from 97 submissions. They are organized in topical sections such as Data analysis, data as a service, services computing, data conversion, data storage, data centers, dataflow architectures, data compression, data exchange, data modeling, databases, and data management.

Elements Of Causal Inference

Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Size: 12.88 MB
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The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.

Machine Learning In Non Stationary Environments

Author: Masashi Sugiyama
Publisher: MIT Press
ISBN: 0262300435
Size: 39.32 MB
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As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Foundations Of Machine Learning

Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 026201825X
Size: 19.68 MB
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Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

Transportation And Information

Author: Piyushimita Vonu Thakuriah
Publisher: Springer
ISBN:
Size: 67.95 MB
Format: PDF
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Transformations in wireless connectivity and location-aware technologies hold the promise of bringing a sea-change in the way transportation information is generated and used in the future. Sensors in the transportation system, when integrated with those in other sectors (for example, energy, utility and health) have the potential to foster novel new ways of improving livability and sustainability. The end-result of these developments has been somewhat contradictory. Although automation in the transportation environment has become increasingly widespread, the level of involvement and active participation by people, in terms of co-creation and contribution of information, has also increased. As a result, the following two major trends have been observed: (1) increases in Machine-to- Machine (M2M) communications; and (2) increases in the variety and volume of User-Generated Content. In this transportation paradigm, the pervasive use of Information and Communication Technologies will serve as the foundation for mobility intelligence towards an “ubiquitous information-centered mobility environment”. However, many technical and operational questions, as well as social, management and legal challenges present themselves in the transformation to this vision. The book presents a non-technical review of research and initiatives and a discussion of such opportunities and challenges.

Self Adaptive Systems For Machine Intelligence

Author: Haibo He
Publisher: John Wiley & Sons
ISBN: 9781118025598
Size: 24.37 MB
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This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.

Los Alamos

Author: Joseph Kanon
Publisher:
ISBN: 9789600325201
Size: 54.15 MB
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Stochastic Algorithms Foundations And Applications

Author: Kathleen Steinhöfel
Publisher: Springer
ISBN:
Size: 33.62 MB
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This book constitutes the refereed proceedings of the International Symposium on Stochastic Algorithms: Foundations and Applications, SAGA 2001, held in Berlin, Germany in December 2001. The nine revised full papers presented together with four invited papers were carefully reviewed and selected for inclusion in the book. The papers are devoted to the design and analysis, experimental evaluation, and real-world application of stochasitc algorithms; in particular, new algorithmic ideas involving stochastic decisions and exploiting probabilistic properties of the underlying problem are introduced. Among the application fields are network and distributed algorithms, local search methods, and computational learning.

Machine Learning

Author: Sergios Theodoridis
Publisher: Academic Press
ISBN: 0128017228
Size: 14.39 MB
Format: PDF, ePub
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This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques – together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.