Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Intelligent Systems
The three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows: Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis; Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications.
Intelligent Systems
The two-volume set LNAI 12319 and 12320 constitutes the proceedings of the 9th Brazilian Conference on Intelligent Systems, BRACIS 2020, held in Rio Grande, Brazil, in October 2020. The total of 90 papers presented in these two volumes was carefully reviewed and selected from 228 submissions. The contributions are organized in the following topical section: Part I: Evolutionary computation, metaheuristics, constrains and search, combinatorial and numerical optimization; neural networks, deep learning and computer vision; and text mining and natural language processing. Part II: Agent and multi-agent systems, planning and reinforcement learning; knowledge representation, logic and fuzzy systems; machine learning and data mining; and multidisciplinary artificial and computational intelligence and applications. Due to the Corona pandemic BRACIS 2020 was held as a virtual event.
MICAI 2005: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, held in Monterrey, Mexico, in November 2005. The 120 revised full papers presented were carefully reviewed and selected from 423 submissions. The papers are organized in topical sections on knowledge representation and management, logic and constraint programming, uncertainty reasoning, multiagent systems and distributed AI, computer vision and pattern recognition, machine learning and data mining, evolutionary computation and genetic algorithms, neural networks, natural language processing, intelligent interfaces and speech processing, bioinformatics and medical applications, robotics, modeling and intelligent control, and intelligent tutoring systems.
Mathematical Reviews
None
Mahout in Action
Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this ...
Geometric Spanner Networks
Aimed at an audience of researchers and graduate students in computational geometry and algorithm design, this book uses the Geometric Spanner Network Problem to showcase a number of useful algorithmic techniques, data structure strategies, and geometric analysis techniques with many applications, practical and theoretical. The authors present rigorous descriptions of the main algorithms and their analyses for different variations of the Geometric Spanner Network Problem. Though the basic ideas behind most of these algorithms are intuitive, very few are easy to describe and analyze. For most of the algorithms, nontrivial data structures need to be designed, and nontrivial techniques need to be developed in order for analysis to take place. Still, there are several basic principles and results that are used throughout the book. One of the most important is the powerful well-separated pair decomposition. This decomposition is used as a starting point for several of the spanner constructions.
Evolutionary Data Clustering: Algorithms and Applications
This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
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.
MICAI ...
None
