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الصفحة 6
Machine Learning Algorithms Using Python Programming
Presents the key concepts of Machine Learning which includes Python concepts and Interpreter, Foundation of Machine Learning, Data Pre-processing, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning, Kernel Machine, Design and analysis of Machine Learning experiment and Data visualization. The theoretical concepts along with coding implementation are covered. This book aims to pursue a middle ground between a theoretical textbook and one that focuses on applications. The book concentrates on the important ideas in machine learning.
Logical Data Modeling : What it is and How to do it
LOGICAL DATA MODELING: What It Is and How To Do IT is directed toward three groups of people: (1) Business subject matter experts, (2) information technology professionals, (3) advanced students in Computer Science, Management Information Systems, and e-Business. Its purpose is to outline the basics of logical data modeling—specifically, data modeling for relational database management systems—in simple, practical terms and in a business context. The focus on relational data modeling is consciously made because it is superior in modeling real business activities.
Logical and Relational Learning
This textbook covers logical and relational learning in depth, and hence provides an introduction to inductive logic programming (ILP), multirelational data mining (MRDM) and (statistical) relational learning (SRL). These subfields of data mining and machine learning are concerned with the analysis of complex and structured data sets that arise in numerous applications, such as bio- and chemoinformatics, network analysis, Web mining, natural language processing, within the rich representations offered by relational databases and computational logic.
Logic Programming with Prolog
Logic Programming is the name given to a distinctive style of programming, very different from that of conventional programming languages such as C++ and Java. By far the most widely used Logic Programming language is Prolog. Prolog is a good choice for developing complex applications, especially in the field of Artificial Intelligence. This book does not assume that the reader is an experienced programmer or has a background in Mathematics, Logic or Artificial Intelligence. It starts from scratch and aims to arrive at the point where quite powerful programs can be written in the language. It is intended both as a textbook for an introductory course and as a self-study book. On completion the reader will know enough to use Prolog in their own research or practical projects. Each chapter has self-assessment exercises so that the reader may check their own progress. A glossary of the technical terms used completes the book.
Logic for Computer Scientists
This book introduces the notions and methods of formal logic from a computer science standpoint, covering propositional logic, predicate logic, and foundations of logic programming. It presents applications and themes of computer science research such as resolution, automated deduction, and logic programming in a rigorous but readable way.The style and scope of the work, rounded out by the inclusion of exercises, make this an excellent textbook for an advanced undergraduate course in logic for computer scientists.
Literature-based Discovery
When Don Swanson hypothesized a connection between Raynaud’s phenomenon and dietary fish oil, the field of literature-based discovery (LBD) was born. During the subsequent two decades a steady stream of researchers have published articles about LBD and the field has made steady progress in laying foundations and creating an identity. LBD is an inherently multi-disciplinary enterprise where collaborations between the information and biomedical sciences are readily encountered. It is the hope and intention that this volume will plant a flag in the ground and inspire new researchers to the LBD challenge.
Linguistics for the age of AI
One of the original goals of artificial intelligence research was to endow intelligent agents with human-level natural language capabilities. Recent AI research, however, has focused on applying statistical and machine learning approaches to big data rather than attempting to model what people do and how they do it. In this book, Marjorie McShane and Sergei Nirenburg return to the original goal of recreating human-level intelligence in a machine. They present a human-inspired, linguistically sophisticated model of language understanding for intelligent agent systems that emphasizes meaning—the deep, context-sensitive meaning that a person derives from spoken or written language.
Legal Programming : Designing Legally Compliant RFID and Software Agent Architectures for Retail Processes and Beyond
LEGAL PROGRAMMING: Designing Legally Compliant RFID and Software Agent Architectures for Retail Processes and Beyond provides a process-oriented discussion of the legal concerns presented by agent-based technologies, processes and programming. It offers a general outline of the potential legal difficulties that could arise in relation to them, focusing on the programming of negotiation and contracting processes in a privacy, consumer and commercial context. The authors will elucidate how it is possible to create form of legal framework and design methodology for transaction agents, applicable in any environment and not just in a specific proprietary framework, that provides the right level of compliance and trust. Key elements considered include the design and programming of legally compliant methods, the determination of rights in respect of objects and variables, and ontologies and programming frameworks for agent interactions. Examples are used to illustrate the points made and provide a practical perspective.
Knowledge Representation and the Semantics of Natural Language
This book presents a method for the semantic representation of natural l- guage expressions (texts, sentences, phrases, etc. ) which can be used as a u- versal knowledge representation paradigm in the human sciences, like lingu- tics, cognitive psychology, or philosophy of language, as well as in com- tational linguistics and in arti?cial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
Addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: Includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. / The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. / Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. / Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. / Considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Journal on Data Semantics IV
• Semantics in data visualization • Semantic services for mobile users • Supporting tools • Applications of semantic-driven approaches These topics are to be understood as specifically related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearance of the journal issues is like the books from the we- known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of the best papers from major conferences addressing data semantics issues. Special issues, focusing on a specific topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board. Finally, it is also possible that a journal issue be devoted to a single text.
Journal on Data Semantics III
– semantic caching – data warehousing and semantic data mining – spatial, temporal, multimedia and multimodal semantics – semantics in data visualization – semantic services for mobile users – supporting tools – applications of semantic-driven approaches These topics are to be understood as speci?cally related to semantic issues. Contributions submitted to the journal and dealing with semantics of data will be considered even if they are not within the topics in the list. While the physical appearance of the journal issues looks like the books from the well-known Springer LNCS series, the mode of operation is that of a journal. Contributions can be freely submitted by authors and are reviewed by the Editorial Board. Contributions may also be invited, and nevertheless carefully reviewed, as in the case for issues that contain extended versions of best papers from major conferences addressing data semantics issues. Special issues, focusing on a speci?c topic, are coordinated by guest editors once the proposal for a special issue is accepted by the Editorial Board. Finally, it is also possible that a journal issue be devoted to a single text.
Java Methods for Financial Engineering : Applications in Finance and Investment
This book is structured around the main theories and models used by practitioners to engineer finance and investment tools. The methods developed and implemented in the text are organized as chapters which cover the core areas.
Java Illuminated ; 5th ed.
Provides learners with an interactive, user-friendly approach to learning the Java programming language. Comprehensive but accessible, the text takes a progressive approach to object-oriented programming, allowing students to build on established skills to develop new and increasingly complex classes. Java Illuminated follows an activity-based active learning approach that ensures student engagement and interest.
Iterating Infusion : Clearer Views of Objects, Classes, and Systems
Iterating Infusion presents comprehensive tools for you to best manage and work with object orientation. These include simplified fundamental concepts, popular language comparisons, advanced designing strategies, a broad usage progression, thorough design notations (interaction algebra), and data-oriented (fundamentally-OO) languages. The title, Iterating Infusion, alludes to the fact that any system has multiple, coexisting functional levels and that new levels—both lower and higher—are continually added to the same functional area. The practical effect is to bring processes into focus, always clarifying the vague. The extreme form of this is when separate but compatible technologies are brought together to create advancements; these can be baby-steps or great leaps, with varying amounts of effort. In more general terms, the same thing in a different context can take on much more power. And actually, this phenomenon is at the heart of object-oriented software.
Computation Engineering : Applied Automata Theory and Logic
This book covers automata in depth, providing good intuitions along the way, and culminating with applications that are used every day in the field. In this respect, it is a departure from the conventional textbooks on complexity and computability, although these 'tradtional' aspects remain well represented.
Component-Based Software Testing with UML
Component-based software development regards software construction in terms of conventional engineering disciplines where the assembly of systems from readily-available prefabricated parts is the norm. Because both component-based systems themselves and the stakeholders in component-based development projects are different from traditional software systems, component-based testing also needs to deviate from traditional software testing approaches. Gross first describes the specific challenges related to component-based testing like the lack of internal knowledge of a component or the usage of a component in diverse contexts. He argues that only built-in contract testing, a test organization for component-based applications founded on building test artifacts directly into components, can prevent catastrophic failures like the one that caused the now famous ARIANE 5 crash in 1996. This book is the first comprehensive treatment of the intricacies of testing component-based software systems. With its strong modeling background, it appeals to researchers and graduate students specializing in component-based software engineering. Professionals architecting and developing component-based systems will profit from the UML-based methodology and the implementation hints based on the XUnit and JUnit frameworks.
Complexity Theory and Cryptology : An Introduction to Cryptocomplexity
Modern cryptology employs mathematically rigorous concepts and methods from complexity theory. Conversely, current research in complexity theory often is motivated by questions and problems arising in cryptology. This book takes account of this trend, and therefore its subject is what may be dubbed "cryptocomplexity,'' some sort of symbiosis of these two areas. This textbook is suitable for undergraduate and graduate students of computer science, mathematics, and engineering, and can be used for courses on complexity theory and cryptology, preferably by stressing their interrelation. Starting from scratch, it is an accessible introduction to cryptocomplexity and works its way to the frontiers of current research. It provides the necessary mathematical background, has numerous figures, exercises, and examples, and presents some central, up-to-date research topics and challenges. Due to its comprehensive bibliography and subject index, it is also a valuable source for researchers, teachers, and practitioners working in these fields.
Complexity Theory : Exploring the Limits of Efficient Algorithms
Complexity theory is the theory of determining the necessary resources for the solution of algorithmic problems and, therefore, the limits of what is possible with the available resources. An understanding of these limits prevents the search for non-existing efficient algorithms. This textbook considers randomization as a key concept and emphasizes the interplay between theory and practice: New branches of complexity theory continue to arise in response to new algorithmic concepts, and its results - such as the theory of NP-completeness - have influenced the development of all areas of computer science. The topics selected have implications for concrete applications, and the significance of complexity theory for today's computer science is stressed throughout.
Compilers : Principles, techniques & tools
This introduction to compilers is the direct descendant of the well-known book by Aho and Ullman, Principles of Compiler Design. The authors present updated coverage of compilers based on research and techniques that have been developed in the field over the past few years. The book provides a thorough introduction to compiler design and covers topics such as context-free grammars, fine state machines, and syntax-directed translation.
