الصفحة 3
الصفحة 3
Hyperparameter tuning for machine and deep learning with R : A practical guide
Equips readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms.
Hybrid metaheuristics ; 5th International Workshop, HM 2008, Málaga, Spain, October 8-9, 2008. Proceedings
This book constitutes the refereed proceedings of the 5th International Workshop on Hybrid Metaheuristics, HM 2008, held in Malaga, Spain, in October 2008.The 14 revised full papers presented were carefully reviewed and selected from 33 submissions. The papers discuss specific aspects of combinations of metaheuristics and other solving techniques for tackling particular relevant constrained optimization problems, such as fiber optic networks, time tabling and freight train scheduling problems.
How Data Quality Affects our Understanding of the Earnings Distribution
This book demonstrates how data quality issues affect all surveys and proposes methods that can be utilised to deal with the observable components of survey error in a statistically sound manner. This book begins by profiling the post-Apartheid period in South Africa's history when the sampling frame and survey methodology for household surveys was undergoing periodic changes due to the changing geopolitical landscape in the country. This book profiles how different components of error had disproportionate magnitudes in different survey years, including coverage error, sampling error, nonresponse error, measurement error, processing error and adjustment error.
High Performance SQL Server " Consistent Response for Mission-Critical Applications
An excellent complement to query performance tuning books and provides the other half of what you need to know by focusing on configuring the instances on which mission-critical queries are executed. You will: Understand SQL Server's database engine and how it processes queries / Configure instances in support of high-throughput applications / Provide consistent response times to varying user numbers and query volumes / Design databases for high-throughput applications with focus on performance / Record performance baselines and monitor SQL Server instances against them / Troubleshot and fix performance problems
High Availability and Disaster Recovery : Concepts, Design, Implementation
Companies and other organizations depend more than ever on the availability of their Information Technology, and most mission critical business processes are IT-based processes. Business continuity is the ability to do business under any circumstances and is an essential requirement modern companies are facing. High availability and disaster recovery are contributions of the IT to fulfill this requirement. And companies will be confronted with such demands to an even greater extent in the future, since their credit ratings will be lower without such precautions. Both, high availability and disaster recovery, are realized by redundant systems. Redundancy can and should be implemented on different abstraction levels: from the hardware, the operating system and middleware components up to the backup computing center in case of a disaster. This book presents requirements, concepts, and realizations of redundant systems on all abstraction levels, and all given examples refer to UNIX and Linux systems.
Heterogeneity in statistical genetics : How to assess, address, and account for mixtures in association studies
Heterogeneity, or mixtures, are ubiquitous in genetics. Even for data as simple as mono-genic diseases, populations are a mixture of affected and unaffected individuals. Still, most statistical genetic association analyses, designed to map genes for diseases and other genetic traits, ignore this phenomenon.In this book, we document methods that incorporate heterogeneity into the design and analysis of genetic and genomic association data. Among the key qualities of our developed statistics is that they include mixture parameters as part of the statistic, a unique component for tests of association. A critical feature of this work is the inclusion of at least one heterogeneity parameter when performing statistical power and sample size calculations for tests of genetic association.
Heavy-Tailed Time Series
This book aims to present a comprehensive, self-contained, and concise overview of extreme value theory for time series, incorporating the latest research trends alongside classical methodology.Additionally, the book incorporates complete proofs and exercises with solutions as well as substantive reference lists and appendices, featuring a novel commentary on the theory of vague convergence.
Hardy Inequalities on Homogeneous Groups : 100 Years of Hardy Inequalities
This book provides an extensive treatment of Hardy inequalities and closely related topics from the point of view of Folland and Stein's homogeneous (Lie) groups. The place where Hardy inequalities and homogeneous groups meet is a beautiful area of mathematics with links to many other subjects.In this environment, the theory of Hardy inequalities becomes intricately intertwined with the properties of sub-Laplacians and subelliptic partial differential equations.
Handbook of Mathematical Models in Computer Vision
In this edited volume we present the most prominent mathematical models that are considered in computational vision. To this end, tasks of increasing complexity are considered and we present the state-of-the-art methods to cope with such tasks. The volume consists of six thematic areas that provide answers to the most dominant questions of computational vision: Image reconstruction, Segmentation and object extraction, Shape modeling and registration, Motion analysis and tracking, 3D from images, geometry and reconstruction Applications in medical image analysis
Handbook of Fractional Calculus for Engineering and Science
Provides reliable methods for solving fractional-order models in science and engineering. Contains efficient numerical methods and algorithms for engineering-related equations. Contains comparison of various methods for accuracy and validity. Demonstrates the applicability of fractional calculus in science and engineering. Examines qualitative as well as quantitative properties of solutions of various types of science- and engineering-related equations.
Grey information : Theory and practical applications
he book covers the latest advances in grey information and systems research, providing a state-of-the-art overview of this important field. Covering the theoretical foundation, fundamental methods and main topics in grey information and systems research, this book includes all the elementary concepts: basic principles, grey numbers and their operations, grey equations and matrices, operators of sequences and generations of grey sequences, grey incidence analysis, grey clusters and grey statistical evaluations, grey systems modeling, grey combined models, grey prediction, grey decisions, grey programming, grey input and output and grey controls, etc.
Grammatical Inference : Algorithms and Applications ; 8th International Colloquium, ICGI 2006, Tokyo, Japan, September 20-22, 2006, Proceedings
The topics discussed range from theoretical results of learning algorithms to innovative applications of grammatical inference and from learning several interesting classes of formal grammars to applications to natural language processing.
Geographic Information Science ; 4th International Conference, GIScience 2006, Münster, Germany, September 20-23, 2006, Proceedings
The GIScience conference series (www. giscience. org) was created as a forum for all researchers who are interested in advancing research in the fundam- tal aspects of geographic information science.The conferences focus on emerging topics and basic research ?ndings across all s- tors of geographic information science. After three highly successful conferences in the United States, this year’s GIScience conference was held in Europe for the ?rst time. The GIScience conferences have been a meeting point for researchers coming from various disciplines, including cognitive science, computer science, engine- ing, geography,information science, mathematics, philosophy, psychology,social science, and statistics. The advancement of geographic information science - quiressuchinterdisciplinarybreadth,andthisisalsowhatmakestheconferences so exciting. In order to account for the di?erent needs of the involved scienti?c disciplines with regard to publishing their research results.
Genetic programming : Theory and practice II
This volume explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The contributions developed from a second workshop at the University of Michigan's Center for the Study of Complex Systems where leading international genetic programming theorists from major universities and active practitioners from leading industries and businesses met to examine how GP theory informs practice and how GP practice impacts GP theory. Chapters include such topics as financial trading rules, industrial statistical model building, population sizing, the roles of structure in problem solving by computer, stock picking, automated design of industrial-strength analog circuits, topological synthesis of robust systems, algorithmic chemistry, supply chain reordering policies, post docking filtering, an evolved antenna for a NASA mission and incident detection on highways.
General Theory of Information Transfer and Combinatorics
This book constitutes the thoroughly refereed research papers contributed to a research project on the `General Theory of Information Transfer and Combinatorics' that was hosted from 2001-2004 at the Center for Interdisciplinary Research (ZIF) of Bielefeld University and also papers of several incorporated meetings thereof. The 63 revised full papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on probabilistic models, cryptology, pseudo random sequences, quantum models, statistics, probability theory, information measures, error concepts, performance criteria, search, sorting, ordering, planning, language evolution, pattern discovery, reconstructions, network coding, combinatorial models, and a problem section.
Fuzzy probabilities : New approach and applications
In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic because probabilities must add to one. Fuzzy random variables have fuzzy distributions. A fuzzy normal random variable has the normal distribution with fuzzy number mean and variance. Applications are to queuing theory, Markov chains, inventory control, decision theory and reliability theory.
Fuzzy logic with engineering applications
With numerous examples and end-of-chapter problems, this book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.
Fundamentals of image data mining : Analysis, features, classification and retrieval
Presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms / Develops many new exercises (most with MATLAB code and instructions) / Includes review summaries at the end of each chapter / Analyses state-of-the-art models, algorithms, and procedures for image mining / Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing / Demonstrates how features like color, texture, and shape can be mined or extracted for image representation / Applies powerful classification approaches: Bayesian classification, support vector machines, neural / networks, and decision trees / Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization
Fundamentals of Artificial Intelligence
Fundamentals of Artificial Intelligence introduces the foundations of present day AI and provides coverage to recent developments in AI such as Constraint Satisfaction Problems, Adversarial Search and Game Theory, Statistical Learning Theory, Automated Planning, Intelligent Agents, Information Retrieval, Natural Language & Speech Processing, and Machine Vision. The book features a wealth of examples and illustrations, and practical approaches along with the theoretical concepts. It covers all major areas of AI in the domain of recent developments. The book is intended primarily for students who major in computer science at undergraduate and graduate level but will also be of interest as a foundation to researchers in the area of AI.
Fundamentals and Methods of Machine and Deep Learning : Algorithms, Tools, and Applications
provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. In recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field.
