Machine Learning Algorithms for Problem Solving in Computational Applications

Computational Multiscale Modeling of Multiphase Nanosystems: Theory and Applications presents a systematic description of the theory of multiscale modeling of nanotechnology applications in various fi

Bridging theory and practice, this book contains over 200 practical exercises and their solutions to train problem-solving abilities in process control.The problems were developed by the author during

Scholars of engineering, business, art, medicine, and other fields explore methods and technologies for teaching creative problem solving in higher education. They discuss the context of higher educat

This book deals with complex problems in the fields of logistics and supply chain management and discusses advanced methods, especially from the field of computational intelligence (CI), for solving t

This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how with the proliferation of massive amounts of unlabeled data, unsupervised learning algorithms, whi

This book covers teaching and learning issues unique to teaching in the science, technology, engineering, and math (STEM) disciplines—such as the extensive coverage of problem-solving—which are not to

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is pres

This book is about the computational aspects of invariant theory. Of central interest is the question how the invariant ring of a given group action can be calculated. Algorithms for this purpos

This book presents powerful techniques for solving global optimization problems on manifolds by means of evolutionary algorithms, and shows in practice how these techniques can be applied to solve rea

Computational and Statistical Methods for Analysing Big Data with Applications begins with an overview of the era of big data. It then goes on to explain different computational and statistical method

This book provides readers with modern computational techniques for solving variety of problems from electrical, mechanical, civil and chemical engineering. Mathematical methods are presented in a uni

Clinical Problem Solving: Case Managementprovides a powerful and unique counseling service format for making recommendations to clients and monitoring cognitive behavioral gains. The Listen Identify-B

This book explains the application of recent advances in computational intelligence – algorithms, design methodologies, and synthesis techniques – to the design of integrated circuits and systems. It

This book presents various recent applications of Artificial Intelligence in Information and Communication Technologies such as Search and Optimization methods, Machine Learning, Data Representation a

This book develops two key machine learning principles: the semi-supervised paradigm and learning with interdependent data. It reveals new applications, primarily web related, that transgress the clas

This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated S

This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, mu

Jumpstart! Thinking Skills and Problem Solving presents a collection of simple to use, multi-sensory games and activities which will jumpstart students’ understanding of problem solving in action. If

Theoretical and Computational Aerodynamics is acomprehensive textbook covering classical aerodynamic theories and recent applications made possible by computational aerodynamics. Logically ordered for

Multi-Agent Machine Learning: A Reinforcement Learning Approach is a framework to understanding different methods and approaches in multi-agent machine learning. It also provides cohesive coverage of

Rooted in a pedagogically successful problem-solving approach to linear algebra, the present work fills a gap in the literature that is sharply divided between elementary texts and books that are too

This book provides readers with modern computational techniques for solving variety of problems from electrical, mechanical, civil and chemical engineering. Mathematical methods are presented in a uni

The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetic Problems provides a detailed and instructional overview of implementing MLFMA. The book:Presents a

This book introduces resource aware image decomposition, registration, fusion, object detection and tracking algorithms along with their applications in security, monitoring and integration in 3rd Gen

This book introduces Converse of Bayes’ Theorem and demonstrates its unexpected applications and points to possible future applications, such as, solving the Bayesian Missing Data Problem (MDP) when t

Offering sequenced guidance for non-specialists on how to reap the benefits of machine learning in medicine and healthcare, this text harnesses the power of cutting-edge computing to maximize the acce

The field of computational sciences has seen a considerable development in mathematics, engineering sciences, and economic equilibrium theory. Researchers in this field are faced with the problem of s

In a rapidly evolving local and global economy, skills related to mathematical problem solving, scientific inquiry, and technological innovation are becoming more critical for success in and out of sc

"Problem-Solving and Selected Topics in Euclidean Geometry: in the Spirit of the Mathematical Olympiads" contains theorems which are of particular value for the solution of geometrical problems. Emph

Tap the best of the crowd in order to put yourself ahead of the competitionIncreasingly, organizations recognize that they require a new foundation for problem-solving and innovation in order to respo

How do issues end up on the agenda? Why do lawmakers routinely invest in program oversight and broad policy development? What considerations drive legislative policy change? For many, Congress is an institution consumed by partisan bickering and gridlock. Yet the institution's long history of addressing significant societal problems - even in recent years - seems to contradict this view. Congress and the Politics of Problem Solving argues that the willingness of many voters to hold elected officials accountable for societal conditions is central to appreciating why Congress responds to problems despite the many reasons mustered for why it cannot. The authors show that, across decades of policy making, problem-solving motivations explain why bipartisanship is a common pattern of congressional behavior and offer the best explanation for legislative issue attention and policy change.

Computational Fluid-Structure Interaction: Methods and Applications takes the reader from the fundamentals of computational fluid and solid mechanics to the state-of-the-art in computational FSI metho

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

This book has evolved from lectures devoted to applications of the Wentzel - Kramers – Brillouin- (WKB or quasi-classical) approximation and of the method of 1/N -expansion for solving various problem

Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications III contains all contributions presented at the International Symposium CompIMAGE 2012 - Computational

"This concise introduction to the methodology of problem solving in organizations is an indispensable guide to the design and execution of practical business improvement projects in real organizationa

Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available—or it beco

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of stat