الصفحة 16
الصفحة 16
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
Describes traditional as well as advanced machine learning algorithms / Enables students to learn which algorithm is most appropriate for the data being handled / Includes numerous, practical case-studies; implementation codes in Python available for readers
Machine Learning for Audio, Image and Video Analysis : Theory and Applications
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text
Machine learning challenges : Evaluating predictive uncertainty, Visual Object Classification, and Recognizing Textual Entailment, 1st Pascal Machine Learning Challenges Workshop, MLCW 2005, Southampton, UK, April 11-13, 2005, Revised Selected Papers
Constitutes the refereed post-proceedings of the First PASCAL Machine Learning Challenges Workshop, MLCW 2005. 25 papers address three challenges: finding an assessment base on the uncertainty of predictions using classical statistics, Bayesian inference, and statistical learning theory; second, recognizing objects from a number of visual object classes in realistic scenes; third, recognizing textual entailment addresses semantic analysis of language to form a generic framework for applied semantic inference in text understanding.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Data Mining in Pattern Recognition ; 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings
Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
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 aspects of computational linguistics ; 4th International Conference, LACL 2001, Le Croisic, France, June 27-29, 2001, Proceedings
Structural Equations in Language Learning.- On the Distinction between Model-Theoretic and Generative-Enumerative Syntactic Frameworks.- Contributed Papers.- A Formal Definition of Bottom-Up Embedded Push-Down Automata and Their Tabulation Technique.- An Algebraic Approach to French Sentence Structure.- Deductive Parsing of Visual Languages.- Lambek Grammars Based on Pregroups.- An Algebraic Analysis of Clitic Pronouns in Italian.- Consistent Identification in the Limit of Any of the Classes k-Valued Is NP-hard.- Polarized Non-projective Dependency Grammars.- On Mixing Deduction and Substitution in Lambek Categorial Grammars.- A Framework for the Hyperintensional Semantics of Natural Language with Two Implementations.- A Characterization of Minimalist Languages.- of Speech Tagging from a Logical Point of View.- Transforming Linear Context-Free Rewriting Systems into Minimalist Grammars.- Recognizing Head Movement.- Combinators for Paraconsistent Attitudes.- Combining Syntax and Pragmatic Knowledge for the Understanding of Spontaneous Spoken Sentences.- Atomicity of Some Categorially Polyvalent Modifiers.
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.
Location- and Context-Awareness ; Vol. 3987 ; 2nd International Workshop, LoCA 2006, Dublin, Ireland, May 10-11, 2006, Proceedings
Contain the papers presented at the 2 International Workshop on Location- and Context-Awareness in May of 2006. As computing moves increasingly into the everyday world, the importance of location and context knowledge grows. The range of contexts encountered while sitting at a desk working on a computer is very limited compared to the large variety of situations experienced away from the desktop. For computing to be relevant and useful in these situations, the computers must have knowledge of the user’s activity, resources, state of mind, and goals, i.e., the user’s context, of which location is an important indicator. This workshop was intended to present research aimed at sensing, inferring, and using location and context data in ways that help the user.
Location- and Context-Awareness ; Vol. 3479 ; First International Workshop, LoCA 2005, Oberpfaffenhofen, Germany, May 12-13, 2005, Proceedings
The workshop was organized by the Institute of Communications and Navigation of the German Aerospace Center (DLR) in Oberpfa?enhofen, and the Mobile and Distributed Systems Group of the University of Munich. During the workshop, novel positioning algorithms and location sensing techniques were discussed, comprising not only enhancements of singular systems, like positioning in GSM or WLAN, but also hybrid technologies, such as the integration of global satellite systems with inertial positioning. Furthermore, improvements in sensor technology, as well as the integration and fusion of sensors, were addressed both on a theoretical and on an implementation level. Personal and confidential data, such as location data of users, have p- found implications for personal information privacy. Thus privacy protection, privacy-oriented location-aware systems, and how privacy aspects the feasibility and usefulness of systems were also addressed in the workshop.
Location- and context-awareness ; 3rd International Symposium, LoCA 2007, Oberpfaffenhofen, Germany, September 20-21, 2007, Proceedings
These proceedings contain the papers presented at the 3rd International S- posium on Location- and Context-Awareness in September of 2007. Computing has become mobile, wireless, and portable. The rangeof contexts encountered while sitting at a desk working on a computer is very limited c- pared to the large variety of situations experienced away from the desktop.
Local Pattern Detection ; International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new field knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the field offers the opportunity to combine the expertise of different fields into a common objective. Moreover, within each field diverse methods have been developed and justified with respect to different quality criteria. We have to investigate how these methods can contributet o solving the problem of KDD. Traditionally, KDD was seeking to end global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to end only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new field of local patterns.
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.
Languages and Compilers for Parallel Computing ; 21th International Workshop, LCPC 2008, Edmonton, Canada, July 31 - August 2, 2008, Revised Selected Papers
This book constitutes the thoroughly refereed post-conference proceedings of the 21th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2008, held in Edmonton, Canada, in July/August 2008.The 18 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 35 submissions. The papers address all aspects of languages, compiler techniques, run-time environments, and compiler-related performance evaluation for parallel and high-performance computing and comprise
Language Technologies for the Challenges of the Digital Age ; 27th International Conference, GSCL 2017, Berlin, Germany, September 13-14, 2017, Proceedings
Constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.
