Seems you have not registered as a member of localhost.saystem.shop!
You may have to register before you can download all our books and magazines, click the sign up button below to create a free account.
Learning from Data
An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
Data-Driven Security
Uncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security p...
MICAI 2002: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the Second Mexican International Conference on Artificial Intelligence, MICAI 2002, held in Mérida, Yucatán, Mexico in April 2002. The 56 revised full papers presented were carefully reviewed and selected from more than 85 submissions from 17 countries. The papers are organized in topical sections on robotics and computer vision, heuristic search and optimization, speech recognition and natural language processing, logic, neural networks, machine learning, multi-agent systems, uncertainty management, and AI tools and applications.
Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applyi...
Learning from Data
Accommodating both beginning and advanced students, this book establishes a general conceptual framework, in which various learning methods, from statistics, neural networks, and fuzzy logic can be applied--showing that a few fundamental principles underlie most new methods being proposed today in the fields of statistics, engineering, and computer science.
Data Engineering
Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.
Design and Analysis of Distributed Algorithms
This text is based on a simple and fully reactive computational model that allows for intuitive comprehension and logical designs. The principles and techniques presented can be applied to any distributed computing environment (e.g., distributed systems, communication networks, data networks, grid networks, internet, etc.). The text provides a wealth of unique material for learning how to design algorithms and protocols perform tasks efficiently in a distributed computing environment.
