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Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.

Computer Science. --- Data Mining and Knowledge Discovery. --- Pattern Recognition. --- Computer science. --- Data mining. --- Optical pattern recognition. --- Informatique --- Exploration de données (Informatique) --- Reconnaissance optique des formes (Informatique) --- Engineering & Applied Sciences --- Computer Science --- Computer algorithms. --- Data structures (Computer science) --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Informatics --- Science --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Algorithms --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination

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The book presents a coherent understanding of computational intelligence from the perspective of what is known as "intelligent computing" with high-dimensional parameters. It critically discusses the central issue of high-dimensional neurocomputing, such as quantitative representation of signals, extending the dimensionality of neuron, supervised and unsupervised learning and design of higher order neurons. The strong point of the book is its clarity and ability of the underlying theory to unify our understanding of high-dimensional computing where conventional methods fail. The plenty of application oriented problems are presented for evaluating, monitoring and maintaining the stability of adaptive learning machine. Author has taken care to cover the breadth and depth of the subject, both in the qualitative as well as quantitative way. The book is intended to enlighten the scientific community, ranging from advanced undergraduates to engineers, scientists and seasoned researchers in computational intelligence.

Engineering. --- Computational Intelligence. --- Pattern Recognition. --- Mathematical Models of Cognitive Processes and Neural Networks. --- Biometrics. --- Optical pattern recognition. --- Ingénierie --- Reconnaissance optique des formes (Informatique) --- Engineering & Applied Sciences --- Computer Science --- Pattern recognition. --- Biometrics (Biology). --- Neural networks (Computer science). --- Computational intelligence. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- Statistics --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Construction --- Industrial arts --- Technology --- Statistical methods --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Neural computers. --- Neural net computers --- Neural network computers --- Neurocomputers --- Electronic digital computers --- Neural networks (Computer science) .

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This book presents a unique collection of state-of-the-art contributions by international remote sensing experts focussing on methodologies to extract information about urban areas from Synthetic Aperture Radar (SAR) data. SAR is an active remote sensing technique capable to gather data independently from sun light and weather conditions. Emphasizing technical and geometrical issues the potential and limits of SAR are addressed in focussed case studies, for example, the detection of buildings and roads, traffic monitoring, surface deformation monitoring, and urban change. These studies can be sorted into two groups: the mapping of the current urban state and the monitoring of change. The former covers, for instance, methodologies for the detection and reconstruction of individual buildings and road networks; the latter, for example, surface deformation monitoring and urban change. This includes also investigations related to the benefit of SAR Interferometry, which is useful to determine either digital elevation models and surface deformation or the radial velocity of objects (e.g. cars), and the Polarization of the signal that comprises valuable information about the type of soil and object geometry. Furthermore, the features of modern satellite and airborne sensor devices which provide high-spatial resolution of the urban scene are discussed. Audience: This book will be of interest to scientists and professionals in geodesy, geography, architecture, engineering and urban planning.

Earth Sciences. --- Remote Sensing/Photogrammetry. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Computer Applications in Earth Sciences. --- Simulation and Modeling. --- Geography. --- Mathematical geography. --- Remote sensing. --- Computer simulation. --- Computer vision. --- Optical pattern recognition. --- Géographie --- Géographique mathématique --- Télédétection --- Simulation par ordinateur --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- GG Cartography --- RMCA --- Synthetic aperture radar --- Urban geography --- SAR (Synthetic aperture radar) --- Remote sensing --- Geography --- Coherent radar --- Geographical information systems. --- Urban geography. --- Geophysics. --- Pattern recognition. --- Optical data processing. --- Geographical Information Systems/Cartography. --- Urban Geography / Urbanism (inc. megacities, cities, towns). --- Geophysics/Geodesy. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Remote-sensing imagery --- Remote sensing systems --- Remote terrain sensing --- Sensing, Remote --- Terrain sensing, Remote --- Aerial photogrammetry --- Aerospace telemetry --- Detectors --- Space optics --- Geological physics --- Terrestrial physics --- Earth sciences --- Physics --- Geographical information systems --- GIS (Information systems) --- Information storage and retrieval systems --- Optical equipment --- Urban geography - Remote sensing --- Synthetic aperture radar.

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Statistical models of shape, learnt from a set of examples, are a widely-used tool in image interpretation and shape analysis. Integral to this learning process is the establishment of a dense groupwise correspondence across the set of training examples. This book gives a comprehensive and up-to-date account of the optimisation approach to shape correspondence, and the question of evaluating the quality of the resulting model in the absence of ground-truth data. It begins with a complete account of the basics of statistical shape models, for both finite and infinite-dimensional representations of shape, and includes linear, non-linear, and kernel-based approaches to modelling distributions of shapes. The optimisation approach is then developed, with a detailed discussion of the various objective functions available for establishing correspondence, and a particular focus on the Minimum Description Length approach. Various methods for the manipulation of correspondence for shape curves and surfaces are dealt with in detail, including recent advances such as the application of fluid-based methods. This complete and self-contained account of the subject area brings together results from a fifteen-year program of research and development. It includes proofs of many of the basic results, as well as mathematical appendices covering areas which may not be totally familiar to some readers. Comprehensive implementation details are also included, along with extensive pseudo-code for the main algorithms. Graduate students, researchers, teachers, and professionals involved in either the development or the usage of statistical shape models will find this an essential resource.

Computer Science. --- Pattern Recognition. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Image Processing and Computer Vision. --- Computer science. --- Computer vision. --- Optical pattern recognition. --- Informatique --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- Mathematical optimization. --- Shape theory (Topology) --Statistical methods. --- Shape theory (Topology) --- Mathematical optimization --- Geometry --- Mathematics --- Physical Sciences & Mathematics --- Statistical methods --- Statistical methods. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Computer graphics. --- Image processing. --- Pattern recognition. --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Homotopy theory --- Mappings (Mathematics) --- Topological manifolds --- Topological spaces --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Optical data processing. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment

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This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Computer Science. --- Pattern Recognition. --- Image Processing and Computer Vision. --- Multimedia Information Systems. --- Computer science. --- Multimedia systems. --- Computer vision. --- Optical pattern recognition. --- Informatique --- Multimédia --- Vision par ordinateur --- Reconnaissance optique des formes (Informatique) --- Electrical Engineering --- Computer Science --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Multimedia information systems. --- Image processing. --- Pattern recognition. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Computer-based multimedia information systems --- Multimedia computing --- Multimedia information systems --- Multimedia knowledge systems --- Information storage and retrieval systems --- Informatics --- Science --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine learning. --- Optical data processing. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Optical equipment --- Pattern perception.