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Knowledge Processing and Data Analysis
This book constitutes the proceedings of the First International Conference on Knowledge - Ontology - Theory (KONT 2007) held in Novosibirsk, Russia, in September 2007 and the First International Conference on Knowledge Processing in Practice (KPP 2007) held in Darmstadt, Germany, in September 2007. The 21 revised full papers were carefully reviewed and selected from numerous submissions and cover four main focus areas: applications of conceptual structures; concept based software; ontologies as conceptual structures; and data analysis.
Artificial Intelligence
This book constitutes the proceedings of the 16th Russian Conference on Artificial Intelligence, RCAI 2018, Moscow, Russia, in September 2018. The 22 full papers presented along with 4 short papers in this volume were carefully reviewed and selected from 75 submissions. The conference deals with a wide range of topics, including data mining and knowledge discovery, text mining, reasoning, decision making, natural language processing, vision, intelligent robotics, multi-agent systems, machine learning, ontology engineering.
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Formal Concept Analysis
Formal concept analysis has been developed as a field of applied mathematics based on the mathematization of concept and concept hierarchy. It thereby allows us to mathematically represent, analyze, and construct conceptual structures. The formal concept analysis approach has been proven successful in a wide range of application fields. This book constitutes a comprehensive and systematic presentation of the state of the art of formal concept analysis and its applications. The first part of the book is devoted to foundational and methodological topics. The contributions in the second part demonstrate how formal concept analysis is successfully used outside of mathematics, in linguistics, text retrieval, association rule mining, data analysis, and economics. The third part presents applications in software engineering.
Introduction to Pattern Recognition
Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. - Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition - Solved examples in Matlab, including real-life data sets in imaging and audio recognition - Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)
Feature Selection for Data and Pattern Recognition
This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reducts; construction of rules and their evaluation; and data- and domain-oriented methodologies.
Feature Selection for Knowledge Discovery and Data Mining
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in ...
