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Smart Graphics
This book constitutes the refereed proceedings of the 6th International Symposium on Smart Graphics, SG 2006, held in Vancouver, Canada, July 2006. The book presents 19 revised full papers and 8 revised short papers. The papers are organized in topical sections on intelligent text processing, perceptive systems, smart visualization, visual features, sketching and graphical abstraction, intelligent image and film composing, as well as smart interaction.
Mining User Generated Content
Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users. Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and...
Foundations of Intelligent Systems
This book constitutes the refereed proceedings of the 18th International Symposium on Methodologies for Intelligent Systems, ISMIS 2009, held in Prague, Czech Republic, in September 2009. The 60 revised papers presented together with 4 plenary talks were carefully reviewed and selected from over 111 submissions. The papers are organized in topical sections on knowledge discovery and data mining, applications and intelligent systems in Medicine, logical and theoretical aspects of intelligent systems, text mining, applications of intelligent sysems in music, information processing, agents, machine learning, applications of intelligent systems, complex data, general AI as well as uncertainty.
Machine Musicianship
Musicians begin formal training by acquiring a body of musical concepts commonly known as musicianship. These concepts underlie the musical skills of listening, performance, and composition. Like humans, computer music programs can benefit from a systematic foundation of musical knowledge. This book explores the technology of implementing musical processes such as segmentation, pattern processing, and interactive improvisation in computer programs. It shows how the resulting applications can be used to accomplish tasks ranging from the solution of simple musical problems to the live performance of interactive compositions and the design of musically responsive installations and Web sites. Ma...
Machine Learning
Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area. See below for earlier volumes in the series.
Computational Music Analysis
This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory...
Data Science and Its Applications
The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operat...
Music and Artificial Intelligence
This book constitutes the refereed proceedings of the Second International Conference on Music and Artificial Intelligence, ICMAI 2002, held in Edinburgh, Scotland, UK in September 2002.The 16 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected for inclusion in the proceedings. Among the topics addressed are parsing for music and language, patterns in music, musical pattern recognition, visualisation, sound classification, tonal structure representation, musical learning systems, pattern analysis, musical perception, melodic segmentation, and time series analysis.
Principles of Data Mining and Knowledge Discovery
This book constitutes the refereed proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD 2001, held in Freiburg, Germany, in September 2001. The 40 revised full papers presented together with four invited contributions were carefully reviewed and selected from close to 100 submissions. Among the topics addressed are hidden Markov models, text summarization, supervised learning, unsupervised learning, demographic data analysis, phenotype data mining, spatio-temporal clustering, Web-usage analysis, association rules, clustering algorithms, time series analysis, rule discovery, text categorization, self-organizing maps, filtering, reinforcemant learning, support vector machines, visual data mining, and machine learning.
