Machine Learning Algorithms for Problem Solving in Computational Applications

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools

The book presents recently developed efficient meta-heuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be us

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, inc

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.

"The only book to approach enzyme kinetics with a problem-solving focus, for practical applications in the food, pharmaceutical, and fine chemistry industry"--

Computational intelligence techniques are becoming more and more important for automated problem solving nowadays. Due to the growing complexity of industrial applications and the increasingly tight t

"This book reviews distance learning programs and software systems, outlining computational methods, algorithms, implemented prototype systems, and applications of open and distance learning"--Provide

Recent research results in the area of parallel algorithms for problem solving, search, natural language parsing, and computer vision, are brought together in this book. The research reported demonstr

A comprehensive reference for researchers in machine learning, data mining, and computer vision, this book presents in-depth, systematic discussions on algorithms and applications for dimensionality r

Decision trees and decision rule systems are widely used in different applicationsas algorithms for problem solving, as predictors, and as a way forknowledge representation. Reducts play key role in t

This book describes numerous applications of the the Dryfus Health Foundation's Problem-Solving for Better Health Program, and related programs, featuring problem solving models used in communities,

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and

Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intellig

Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and res

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabiliti

Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.In this book, you will:Use heuristics and design fitness functions.Build genetic algorithms.Make nature-inspired swarms with ants, bees and particles.Create Monte Carlo simulations.Investigate cellular automata.Find minima and maxima, using hill climbing and simulated annealing.Try selection methods,

3 comprehensive manuscripts in 1 bookMachine Learning: An Essential Guide to Machine Learning for Beginners Who Want to Understand Applications, Artificial Intelligence, Data Mining, Big Data and More

Algebraic and Combinatorial Computational Biology introduces students and researchers to a panorama of powerful and current methods for mathematical problem-solving in modern computational biology. Pr

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different ap

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning,

This volume presents a comprehensive treatment of Riemannian geometry as a mathematical and computational framework for many problems in machine learning, statistics, optimization, and computer vision

A new, intuitive approach to machine learning, covering fundamental concepts and real-world applications, with practical MATLAB-based exercises.

By teaching you how to code machine-learning algorithms using a test-driven approach, this practical book helps you gain the confidence you need to use machine learning effectively in a business envir

This book provides a concept-based problem-solving learning approach to mastering practical spatial analysis tasks. It draws from statistics, spatial statistics, visualization, and computational metho

"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying form

Learning, Problem Solving, and Mindtools is inspired by the substantial body of learning research by David H. Jonassen in the areas of mind tools and problem solving. The focus of the volume is on ed

Learning, Problem Solving, and Mindtools is inspired by the substantial body of learning research by David H. Jonassen in the areas of mind tools and problem solving. The focus of the volume is on ed

With a focus on developing computational algorithms for examining waveform design in diverse active sensing applications, this guide is ideal for researchers and practitioners in the field. The three parts conveniently correspond to the three categories of desirable waveform properties: good aperiodic correlations, good periodic correlations and beampattern matching. The book features various application examples of using the newly designed waveforms, including radar imaging, channel estimation for communications, an ultrasound system for breast cancer treatment and covert underwater communications. In addition to numerical results, the authors present theoretical analyses describing lower bounds or limitations of performance. Focusing on formulating practical problems mathematically and solving the mathematical problems using efficient and effective optimization techniques, the text pays particular attention to developing easy-to-use computational approaches. Most algorithms are accom

Breakthrough Problem Solving with Action Learning explores why and how action learning groups have been so successful and creative in solving complex problems. The text begins by briefly reviewing the

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors,

With an emphasis on problem solving, this book introduces the basic principles and fundamental concepts of computational modeling. It emphasizes reasoning and conceptualizing problems, the elementary

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for c

PData Structures and Problem Solving Using C++ provides a practical introduction to data structures and algorithms from the viewpoint of abstract thinking and problem solving, as well as the use of C+

This second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. It

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional str

Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.