Download or read book Minimum Error Entropy Classification written by Joaquim P. Marques de Sá and published by Springer. This book was released on 2012-07-25 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals. Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions.

Download or read book Kalman Filtering Under Information Theoretic Criteria written by Badong Chen and published by Springer Nature. This book was released on 2023-09-19 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.

Download or read book Information Theoretic Learning written by Jose C. Principe and published by Springer Science & Business Media. This book was released on 2010-04-06 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Download or read book Impact of Scientific Computing on Science and Society written by Pekka Neittaanmäki and published by Springer Nature. This book was released on 2023-07-07 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book analyzes the impact of scientific computing in science and society over the coming decades. It presents advanced methods that can provide new possibilities to solve scientific problems and study important phenomena in society. The chapters cover Scientific computing as the third paradigm of science as well as the impact of scientific computing on natural sciences, environmental science, economics, social science, humanistic science, medicine, and engineering. Moreover, the book investigates scientific computing in high performance computing, quantum computing, and artificial intelligence environment and what it will be like in the 2030s and 2040s.

Download or read book System Parameter Identification written by Badong Chen and published by Newnes. This book was released on 2013-07-17 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, criterion functions based on information theoretic measures (entropy, mutual information, information divergence) have attracted attention and become an emerging area of study in signal processing and system identification domain. This book presents a systematic framework for system identification and information processing, investigating system identification from an information theory point of view. The book is divided into six chapters, which cover the information needed to understand the theory and application of system parameter identification. The authors’ research provides a base for the book, but it incorporates the results from the latest international research publications. Named a 2013 Notable Computer Book for Information Systems by Computing Reviews One of the first books to present system parameter identification with information theoretic criteria so readers can track the latest developments Contains numerous illustrative examples to help the reader grasp basic methods

Download or read book Progress in Artificial Intelligence written by Luis Miguel Correia and published by Springer. This book was released on 2013-09-04 with total page 570 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, held in Angra do Heroísmo, Azores, Portugal, in September 2013. The 45 revised full papers presented were carefully reviewed and selected from a total of 157 submissions. The papers are organized in the following topical sections: ambient intelligence and affective environments; artificial intelligence in transportation systems; artificial life and evolutionary algorithms; computational methods in bioinformatics and systems biology; general artificial intelligence; intelligent robotics; knowledge discovery and business intelligence; multi-agent systems: theory and applications; social simulation and modeling; and text mining and applications.

Download or read book Progress in Pattern Recognition Image Analysis Computer Vision and Applications written by César San Martin and published by Springer Science & Business Media. This book was released on 2011-10-28 with total page 736 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 16th Iberoamerican Congress on Pattern Recognition, CIARP 2011, held in Pucón, Chile, in November 2011. The 81 revised full papers presented together with 3 keynotes were carefully reviewed and selected from numerous submissions. Topics of interest covered are image processing, restoration and segmentation; computer vision; clustering and artificial intelligence; pattern recognition and classification; applications of pattern recognition; and Chilean Workshop on Pattern Recognition.

Download or read book Adaptive Filtering Under Minimum Mean p Power Error Criterion written by Wentao Ma and published by CRC Press. This book was released on 2024-05-31 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive filtering still receives attention in engineering as the use of the adaptive filter provides improved performance over the use of a fixed filter under the time-varying and unknown statistics environments. This application evolved communications, signal processing, seismology, mechanical design, and control engineering. The most popular optimization criterion in adaptive filtering is the well-known minimum mean square error (MMSE) criterion, which is, however, only optimal when the signals involved are Gaussian-distributed. Therefore, many "optimal solutions" under MMSE are not optimal. As an extension of the traditional MMSE, the minimum mean p-power error (MMPE) criterion has shown superior performance in many applications of adaptive filtering. This book aims to provide a comprehensive introduction of the MMPE and related adaptive filtering algorithms, which will become an important reference for researchers and practitioners in this application area. The book is geared to senior undergraduates with a basic understanding of linear algebra and statistics, graduate students, or practitioners with experience in adaptive signal processing. Key Features: Provides a systematic description of the MMPE criterion. Many adaptive filtering algorithms under MMPE, including linear and nonlinear filters, will be introduced. Extensive illustrative examples are included to demonstrate the results.

Download or read book Pattern Recognition and Image Analysis written by J. Salvador Sánchez and published by Universitat Jaume I. This book was released on 2001 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Intelligent Data Processing written by Vadim V. Strijov and published by Springer Nature. This book was released on 2019-11-15 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Processing, IDP 2016, held in Barcelona, Spain, in October 2016. The 11 revised full papers were carefully reviewed and selected from 52 submissions. The papers of this volume are organized in topical sections on machine learning theory with applications; intelligent data processing in life and social sciences; morphological and technological approaches to image analysis.

Download or read book Artificial Neural Networks in Cancer Diagnosis Prognosis and Patient Management written by R. N. G. Naguib and published by CRC Press. This book was released on 2001-06-22 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: The potential value of artificial neural networks (ANN) as a predictor of malignancy has begun to receive increased recognition. Research and case studies can be found scattered throughout a multitude of journals. Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management brings together the work of top researchers - primaril

Download or read book Condition Monitoring with Vibration Signals written by Asoke K. Nandi and published by John Wiley & Sons. This book was released on 2019-10-16 with total page 440 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 monitoring—guiding 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 Information Theory and Statistical Learning written by Frank Emmert-Streib and published by Springer Science & Business Media. This book was released on 2009 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: This interdisciplinary text offers theoretical and practical results of information theoretic methods used in statistical learning. It presents a comprehensive overview of the many different methods that have been developed in numerous contexts.

Download or read book Entropy Application for Forecasting written by Ana Jesus Lopez-Menendez and published by MDPI. This book was released on 2020-12-29 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows the potential of entropy and information theory in forecasting, including both theoretical developments and empirical applications. The contents cover a great diversity of topics, such as the aggregation and combination of individual forecasts, the comparison of forecasting performance, and the debate concerning the tradeoff between complexity and accuracy. Analyses of forecasting uncertainty, robustness, and inconsistency are also included, as are proposals for new forecasting approaches. The proposed methods encompass a variety of time series techniques (e.g., ARIMA, VAR, state space models) as well as econometric methods and machine learning algorithms. The empirical contents include both simulated experiments and real-world applications focusing on GDP, M4-Competition series, confidence and industrial trend surveys, and stock exchange composite indices, among others. In summary, this collection provides an engaging insight into entropy applications for forecasting, offering an interesting overview of the current situation and suggesting possibilities for further research in this field.

Download or read book Quantitative analysis of neuroanatomy written by Julian M L Budd and published by Frontiers Media SA. This book was released on 2016-03-22 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The true revolution in the age of digital neuroanatomy is the ability to extensively quantify anatomical structures and thus investigate structure-function relationships in great detail. Large-scale projects were recently launched with the aim of providing infrastructure for brain simulations. These projects will increase the need for a precise understanding of brain structure, e.g., through statistical analysis and models. From articles in this Research Topic, we identify three main themes that clearly illustrate how new quantitative approaches are helping advance our understanding of neural structure and function. First, new approaches to reconstruct neurons and circuits from empirical data are aiding neuroanatomical mapping. Second, methods are introduced to improve understanding of the underlying principles of organization. Third, by combining existing knowledge from lower levels of organization, models can be used to make testable predictions about a higher-level organization where knowledge is absent or poor. This latter approach is useful for examining statistical properties of specific network connectivity when current experimental methods have not yet been able to fully reconstruct whole circuits of more than a few hundred neurons.

Download or read book Big data analytics for smart healthcare applications written by Celestine Iwendi and published by Frontiers Media SA. This book was released on 2023-04-17 with total page 1365 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the 4th International Conference on Big Data Analytics for Cyber Physical System in Smart City Volume 1 written by Mohammed Atiquzzaman and published by Springer Nature. This book was released on 2023-07-04 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a selection of peer-reviewed papers presented at the 4th Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2022) conference, held in Bangkok, Thailand, on December 16–17. The contributions, prepared by an international team of scientists and engineers, cover the latest advances and challenges made in the field of big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems.