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Intelligent Data Engineering and Automated Learning - IDEAL 2004
This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004, held in Exeter, UK, in August 2004. The 124 revised full papers presented were carefully reviewed and selected from 272 submissions. The papers are organized in topical sections on bioinformatics, data mining and knowledge engineering, learning algorithms and systems, financial engineering, and agent technologies.
Angiography and Plaque Imaging
Recent, rapid advances in mathematical engineering and applied mathematics have opened the door to solving complex problems in angiography imaging. For the first time, this book presents the different medical imaging modalities--MR, CT, x-ray, and ultrasound--for performing angiography and its analysis. Pioneers from a variety of relevant disciplin
Handbook of Biomedical Image Analysis
Stereo and temporal eye registration by mutual information maximization -- Quantification of brain aneurysm dimensions from CTA for surgical planning of coiling interventions -- Inverse consistent image registration -- A computer-aided design system for segmentation of volumetric images -- Inter-subject non-rigid registration: an overview with classification and the Romeo algorithm -- Elastic registration for biomedical applications -- Quo vadis, atlas-based segmentation -- Elastic registration for biomedical applications --
Computer Vision Beyond the Visible Spectrum
Recently, there has been a dramatic increase in the use of sensors in the non-visible bands. As a result, there is a need for existing computer vision methods and algorithms to be adapted for use with non-visible sensors, or for the development of completely new methods and systems. Computer Vision Beyond the Visible Spectrum is the first book to bring together state-of-the-art work in this area. It presents new & pioneering research across the electromagnetic spectrum in the military, commercial, and medical domains. By providing a detailed examination of each of these areas, it focuses on the development of state-of-the-art algorithms and looks at how they can be used to solve existing & new challenges within computer vision. Essential reading for academics & industrial researchers working in the area of computer vision, image processing, and medical imaging, it will also be useful background reading for advanced undergraduate & postgraduate students.
Support Vector Machines for Pattern Classification
I was shocked to see a student’s report on performance comparisons between support vector machines (SVMs) and fuzzy classi?ers that we had developed withourbestendeavors.Classi?cationperformanceofourfuzzyclassi?erswas comparable, but in most cases inferior, to that of support vector machines. This tendency was especially evident when the numbers of class data were small. I shifted my research e?orts from developing fuzzy classi?ers with high generalization ability to developing support vector machine–based classi?ers. This book focuses on the application of support vector machines to p- tern classi?cation. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines. To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers interested in the theoretical aspect of support vector machines should refer to books such as [109, 215, 256, 257].
Debugging Machine Learning Models with Python
Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world success Key Features Learn how to improve performance of your models and eliminate model biases Strategically design your machine learning systems to minimize chances of failure in production Discover advanced techniques to solve real-world challenges Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the ex...
Intelligent Data Engineering and Automated Learning - IDEAL 2006
This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006. The 170 revised full papers presented were carefully selected from 557 submissions. The papers are organized in topical sections on learning and information processing, data mining, retrieval and management, bioinformatics and bio-inspired models, agents and hybrid systems, financial engineering, as well as a special session on nature-inspired date technologies.
Responsible AI in the Enterprise
Build and deploy your AI models successfully by exploring model governance, fairness, bias, and potential pitfalls Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn ethical AI principles, frameworks, and governance Understand the concepts of fairness assessment and bias mitigation Introduce explainable AI and transparency in your machine learning models Book DescriptionResponsible AI in the Enterprise is a comprehensive guide to implementing ethical, transparent, and compliant AI systems in an organization. With a focus on understanding key concepts of machine learning models, this book equips you with techniques and algorithms to tackle complex issues such as...
Generalized Polygons
This book is intended to be an introduction to the fascinating theory ofgeneralized polygons for both the graduate student and the specialized researcher in the field. It gathers together a lot of basic properties (some of which are usually referred to in research papers as belonging to folklore) and very recent and sometimes deep results. I have chosen a fairly strict geometrical approach, which requires some knowledge of basic projective geometry. Yet, it enables one to prove some typically group-theoretical results such as the determination of the automorphism groups of certain Moufang polygons. As such, some basic group-theoretical knowledge is required of the reader. The notion of a gen...
Statistical and Neural Classifiers
The classification of patterns is an important area of research which is central to all pattern recognition fields, including speech, image, robotics, and data analysis. Neural networks have been used successfully in a number of these fields, but so far their application has been based on a 'black box approach' with no real understanding of how they work. In this book, Sarunas Raudys - an internationally respected researcher in the area - provides an excellent mathematical and applied introduction to how neural network classifiers work and how they should be used.. .
