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Feature Engineering for Machine Learning
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, st...
A Survey of Statistical Network Models
Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active network community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. ...
The Practical Handbook of Internet Computing
The Practical Handbook of Internet Computing analyzes a broad array of technologies and concerns related to the Internet, including corporate intranets. Fresh and insightful articles by recognized experts address the key challenges facing Internet users, designers, integrators, and policymakers. In addition to discussing major applications, it also covers the architectures, enabling technologies, software utilities, and engineering techniques that are necessary to conduct distributed computing and take advantage of Web-based services. The Handbook provides practical advice based upon experience, standards, and theory. It examines all aspects of Internet computing in wide-area and enterprise ...
Robust Latent Feature Learning for Incomplete Big Data
Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, th...
Information Security
The 2004 Information Security Conference was the seventh in a series that started with the Information Security Workshop in 1997. A distinct feature of this series is the wide coverage of topics with the aim of encouraging interaction between researchers in di?erent aspects of information security. This trend c- tinuedintheprogramofthisyear’sconference.Theprogramcommitteereceived 106 submissions, from which 36 were selected for presentation. Each submission was reviewed by at least three experts in the relevant research area. We would liketothankalltheauthorsfortakingtheirtimetopreparethesubmissions,and wehopethatthosewhosepapersweredeclinedwillbeableto?ndanalternative forum for their work...
Information Security
This book constitutes the refereed proceedings of the Third International Workshop on Information Security, ISW 2000, held in Wollongong, Australia in December 2000. The 23 revised full papers presented were carefully reviewed and selected from 63 submissions. The papers are organized in topical sections on multimedia copyright protection, e-commerce, key management, network security and access control, and cryptographic systems.
Network Analysis
‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.
Information Security and Cryptology - ICISC 2002
This book constitutes the thoroughly refereed post-proceedings of the 5th International Conference on Information Security and Cryptology, ICISC 2002, held in Seoul, Korea in November 2002. The 35 revised full papers presented together with an invited paper were carefully selected from 142 submissions during two rounds of reviewing and improvement. The papers are organized in topical sections on digital signatures, Internet security, block ciphers and stream ciphers, stream ciphers and other primitives, efficient implementations, side-channel attacks, cryptographic protocols and biometrics.
Algebraic and Geometric Methods in Discrete Mathematics
This volume contains the proceedings of the AMS Special Session on Algebraic and Geometric Methods in Applied Discrete Mathematics, held on January 11, 2015, in San Antonio, Texas. The papers present connections between techniques from “pure” mathematics and various applications amenable to the analysis of discrete models, encompassing applications of combinatorics, topology, algebra, geometry, optimization, and representation theory. Papers not only present novel results, but also survey the current state of knowledge of important topics in applied discrete mathematics. Particular highlights include: a new computational framework, based on geometric combinatorics, for structure predicti...
Statistical Network Analysis: Models, Issues, and New Directions
This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.
