Algorithms

Provides state-of-the-art algorithms for sound capture, processing and enhancement Sound Capture and Processing: Practical Approaches covers the digital signal processing algorithms and devices for c

Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural w

An introduction to hidden Markov models for the dynamical systems community. The book presents algorithms for using HMMs and explains the derivation of those algorithms. It provides introductory mater

Gamut mapping algorithms, implemented by color management systems, are an integral part of the color reproduction process. By adjusting the colors with appropriate algorithms, gamut mapping enables or

This book strives to provide a balanced coverage of efficient algorithms commonly used in solving mathematical optimization problems. It covers both the convectional algorithms and modern heuristic a

This book starts with illustrations of the ubiquitous character of optimization, and describes numerical algorithms in a tutorial way. It covers fundamental algorithms as well as more specialized and

This book provides the reader with a fundamental understanding of global illumination algorithms. It discusses a broad class of algorithms for realistic image synthesis and introduces a theoretical b

The Lanczos and conjugate gradient (CG) algorithms are fascinating numerical algorithms. This book presents the most comprehensive discussion to date of the use of these methods for computing eigenval

This text teaches the techniques needed to analyze algorithms. Organized by analysis techniques, The Analysis of Algorithms includes a systematic and largely self-contained treatment of the mathematic

This handbook explains basic and advanced concepts of image processing, provides a collection of algorithms, and demonstrates the algorithms with real-world scientific and technical applications. Upda

A detailed examination of learning classifier systems (LCS), a form of machine learning system, which incorporates both Evolutionary Algorithms and Reinforcement Learning Algorithms.

This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike tra

An introduction to the algorithms of computer algebra. These algorithms are discussed from both a theoretical and practical perspective. Those cases where theoretically optimal algorithms are inapprop

Robert Dale presents a detailed description of the development of algorithms for the generation of referring expressions, and of the underlying structures that motivate these algorithms, in a dynamic

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or usin

Applications of Computational Intelligence in Multi-Disciplinary Research provides readers with a comprehensive handbook for applying the powerful principles, concepts and algorithms of Computational

Optimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; found

Statistically-derived algorithms, adopted by many jurisdictions in an effort to identify the risk of reoffending posed by criminal defendants, have been lambasted as racist, de-humanizing, and antithetical to the foundational tenets of criminal justice. Just Algorithms argues that these attacks are misguided and that, properly regulated, risk assessment tools can be a crucial means of safely and humanely dismantling our massive jail and prison complex. The book explains how risk algorithms work, the types of legal questions they should answer, and the criteria for judging whether they do so in a way that minimizes bias and respects human dignity. It also shows how risk assessment instruments can provide leverage for curtailing draconian prison sentences and the plea-bargaining system that produces them. The ultimate goal of Christopher Slobogin's insightful analysis is to develop the principles that should govern, in both the pretrial and sentencing settings, the criminal justice syste

Statistically-derived algorithms, adopted by many jurisdictions in an effort to identify the risk of reoffending posed by criminal defendants, have been lambasted as racist, de-humanizing, and antithetical to the foundational tenets of criminal justice. Just Algorithms argues that these attacks are misguided and that, properly regulated, risk assessment tools can be a crucial means of safely and humanely dismantling our massive jail and prison complex. The book explains how risk algorithms work, the types of legal questions they should answer, and the criteria for judging whether they do so in a way that minimizes bias and respects human dignity. It also shows how risk assessment instruments can provide leverage for curtailing draconian prison sentences and the plea-bargaining system that produces them. The ultimate goal of Christopher Slobogin's insightful analysis is to develop the principles that should govern, in both the pretrial and sentencing settings, the criminal justice syste

Providing the first truly comprehensive overview of Network Tomography - a novel network monitoring approach that makes use of inference techniques to reconstruct the internal network state from external vantage points - this rigorous yet accessible treatment of the fundamental theory and algorithms of network tomography covers the most prominent results demonstrated on real-world data, including identifiability conditions, measurement design algorithms, and network state inference algorithms, alongside practical tools for applying these techniques to real-world network management. It describes the main types of mathematical problems, along with their solutions and properties, and emphasizes the actions that can be taken to improve the accuracy of network tomography. With proofs and derivations introduced in an accessible language for easy understanding, this is an essential resource for professional engineers, academic researchers, and graduate students in network management and netwo

A multidisciplinary introduction to the field of computational creativity, analyzing the impact of advanced generative technologies on art and music.As algorithms get smarter, what role will computers