Libros de Informática práctica, página 4 de 413

Language Engineering examines the processes involved in dictionary-making, parsing, spell-checking, lexical semantics, machine translation, question answering and text attribution. The dictionary-building and natural language processing techniques examined are applicable to most languages, and the book includes extensive examples in English, French and German.This book is the first detailed description of morphology, syntax and semantics using the C(C++) programming language, and as such should be essential reading for researchers in natural language processing and computational linguistics.

'The coverage is unparalleled in both depth and breadth. No other text that I have seen offers a better complete overview of modern robotic manipulation and robot control' - Bradley Bishop, United States Naval Academy. Based on the highly successful classic, "Robot Dynamics and Control", by Spong and Vidyasagar (Wiley, 1989), "Robot Modeling and Control" offers a thoroughly up-to-date, self-contained introduction to the field. The text presents basic and advanced material in a style that is at once readable and mathematically rigorous. Key features of this title include: a step-by-step computational approach helps you derive and compute the forward kinematics, inverse kinematics, and Jacobians for the most common robot designs; detailed coverage of vision and visual servo control enables you to program robots to manipulate objects sensed by cameras; and, an entire chapter on dynamics prepares you to compute the dynamics of the most common manipulator designs. The most common motion planning and trajectory generation algorithms are presented in an elementary style. The comprehensive treatment of motion and force control includes both basic and advanced methods. The text's treatment of geometric nonlinear control is more readable than in more advanced texts. Many worked examples and an extensive list of problems illustrate all aspects of the theory. About the authors Mark W. Spong is Donald Biggar Willett Professor of Engineering at the University of Illinois at Urbana-Champaign. Dr. Spong is the 2005 President of the IEEE Control Systems Society and past Editor-in-Chief of the "IEEE Transactions on Control Systems Technology". Seth Hutchinson is currently a Professor at the University of Illinois in Urbana-Champaign, and a senior editor of the "IEEE Transactions on Robotics and Automation". He has published extensively on the topics of robotics and computer vision. Mathukumalli Vidyasagar is currently Executive Vice President in charge of Advanced Technology at Tata Consultancy Services (TCS), India's largest IT firm. Dr. Vidyasagar was formerly the director of the Centre for Artificial Intelligence and Robotics (CAIR), under Government of India's Ministry of Defense

Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications. Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning. Show more Show less

Empowering IoT with Big Data Analytics provides comprehensive coverage of major topics tools and techniques related to empowering IoT with big data technologies and big data analytics solutions thus allowing for better processing analysis protection distribution and visualization of data for the benefit of IoT applications and second a better deployment of IoT applications on the ground This book covers big data in the IoT era its application domains current state of the art in big data and IoT technologies standards platforms and solutions This book provides a holistic view of the big data value chain for IoT including storage processing protection distribution analytics and visualization Big data is a multi disciplinary topic involving handling intensive continuous and heterogeneous data retrieved from different sources including sensors social media and embedded systems The emergence of Internet of Things IoT and its application to many domains has led to the generation of huge amounts of both structured and unstructured data often referred to as big data

One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own.The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers.Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics.The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.