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Artificial Neural Networks in Pattern Recognition
This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.
Analysis of Large and Complex Data
This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.
Self-Organizing Neural Networks
The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of interna tional researchers. A number of extensions and modifications have been developed during the last two decades. The reason is surely not that the original algorithm was imperfect or inad equate. It is rather the universal applicability and easy handling of the SOM. Com pared to many other network paradigms, only a few parameters need to be arranged and thus also for a beginner ...
Challenges at the Interface of Data Analysis, Computer Science, and Optimization
This volume provides approaches and solutions to challenges occurring at the interface of research fields such as data analysis, computer science, operations research, and statistics. It includes theoretically oriented contributions as well as papers from various application areas, where knowledge from different research directions is needed to find the best possible interpretation of data for the underlying problem situations. Beside traditional classification research, the book focuses on current interests in fields such as the analysis of social relationships as well as statistical musicology.
Systems Biology of Cell Signaling
Topic Editor Prof. Xing is in collaboration with ATCC (https://www.atcc.org/) on testing some of their cell lines in research. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Structural, Syntactic, and Statistical Pattern Recognition
This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2020, held in Padua, Italy, in January 2021. The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions. The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.
Information- and Communication Theory in Molecular Biology
This edited monograph presents the collected interdisciplinary research results of the priority program “Information- and Communication Theory in Molecular Biology (InKoMBio, SPP 1395)”, funded by the German Research Foundation DFG, 2010 until 2016. The topical spectrum is very broad and comprises, but is not limited to, aspects such as microRNA as part of cell communication, information flow in mammalian signal transduction pathway, cell-cell communication, semiotic structures in biological systems, as well as application of methods from information theory in protein interaction analysis. The target audience primarily comprises research experts in the field of biological signal processing, but the book is also beneficial for graduate students alike.
Artificial Neural Networks in Pattern Recognition
This book constitutes the refereed proceedings of the 5th INNS IAPR TC3 GIRPR International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2012, held in Trento, Italy, in September 2012. The 21 revised full papers presented were carefully reviewed and selected for inclusion in this volume. They cover a large range of topics in the field of neural network- and machine learning-based pattern recognition presenting and discussing the latest research, results, and ideas in these areas.
