Die 5. Osterreichische Artificial-Intelligence-Tagung setzt sich zusammen aus wissenschaftlichem Programm, Workshops und Tutorials. Der wissenschaftlich orientierte Teil des Tagungsprogramms umfasst sowohl eingeladene als auch begutachtete Vortrage zu den Themen Qualitatives Schliessen, Methodik Wissensbasierter Systeme und deren Anwendung, Logik/Deduktion, Naturlichsprachliche Systeme, Lernen und Kognition. Zum Informationsaustausch waren zusatzlich Workshops zur Weiterbildung vorgesehen. Besonders das Thema "Philosophie und KI" demonstrierte das allgemeine Interesse. Dies soll mit Beitragen dokumentiert werden, die einen Uberblick uber Beruhrungspunkte der KI mit philosophischen Stromungen bieten und auch den Einfluss der KI als Teil der Informatik auf das philosophische Weltbild verdeutlichen. Ebenfalls reprasentative Beitrage wurden zu den Workshops "Konnektionismus", "Qualitatives Schliessen" und "Begriffsbildung/-modellierung" ausgewahlt.

Pattern recognition is traditionally considered to cover all aspects of sensory data perception ranging from data acquisition, through preprocessing and low level analysis, to high level interpretation. Owing to its breadth and important application potential, the field of pattern recognition has been attracting considerable attention of researchers in academia and industry and consequently it has been witnessing a rapid growth and perpetual development. The need for dissemination of the latest results is being served by a host of international conferences on pattern recognition. One such series of meetings is regularly held in the United Kingdom under the auspices of the British Pattern Recognition Association. This volume contains papers presented at the BPRA 4th International Conference on Pattern Recognition held in Cambridge, March 28-30, 1988. Alongside the conventional topics of statistical and syntactic pattern recognition, contributions address issues in the hot subject areas of adaptive learning networks, computer vision, knowledge base methods and architectures for pattern processing, and among others, report progress in the application domains of document processing, speech and text recognition and shape analysis for industrial robotics. It is believed that the collection is not merely a report on current activities but that it will also be an important source of inspiration for future developments in the field of pattern recognition.

Software design patterns are known to play a vital role in enhancing the quality of software systems while reducing development time and cost. However, the use of these design patterns has also been known to introduce problems that can significantly reduce the stability, robustness, and reusability of software. This book introduces a new process for creating software design patterns that leads to highly stable, reusable, and cost-effective software. The basis of this new process is a topology of software patterns called knowledge maps. This book provides readers with a detailed view of the art and practice of creating meaningful knowledge maps. It demonstrates how to classify software patterns within knowledge maps according to their application rationale and nature. It provides readers with a clear methodology in the form of step-by-step guidelines, heuristics, and quality factors that simplify the process of creating knowledge maps. This book is designed to allow readers to master the basics of knowledge maps from their theoretical aspects to practical application. It begins with an overview of knowledge map concepts and moves on to knowledge map goals, capabilities, stable design patterns, development scenarios, and case studies. Each chapter of the book concludes with an open research issue, review questions, exercises, and a series of projects.

Das Buch fuhrt auf einfache und verstandliche Weise in die Bayes-Statistik ein. Ausgehend vom Bayes-Theorem werden die Schatzung unbekannter Parameter, die Festlegung von Konfidenzregionen fur die unbekannten Parameter und die Prufung von Hypothesen fur die Parameter abgeleitet. Angewendet werden die Verfahren fur die Parameterschatzung im linearen Modell, fur die Parameterschatzung, die sich robust gegenuber Ausreissern in den Beobachtungen verhalt, fur die Pradiktion und Filterung, die Varianz- und Kovarianzkomponentenschatzung und die Mustererkennung. Fur Entscheidungen in Systemen mit Unsicherheiten dienen Bayes-Netze. Lassen sich notwendige Integrale analytisch nicht losen, werden numerische Verfahren mit Hilfe von Zufallswerten eingesetzt."

Der vorliegende Tagungsband enthalt die meisten Vortrage der gemeinsamen Tagung der Deutschen Gesellschaft fUr Angewandte Optik (DGaO) und der Deutschen Arbeits gemeinschaft fUr Musterkennung (DAr, M). Die Beziehungen der beiden Organisationen sind folgendermaBen: Wahrend die DGaO eine traditionsreiche wissenschaftliche Gesellschaft ist, stellt die DAGM einen problemorientierten Dachverband folgender wissenschaftlicher Gesellschaften dar: - Deutsche Gesellschaft fUr angewandte Optik (DGaO) - Deutsche Gesellschaft fUr Nuklearmedizin (DGNM) - Deutsche Gesellschaft fUr Ortung und Navigation (DGON) - Deutsche Gesellschaft fUr Medizinische Dokumentation, Informatik und Statistik (GMDS) - Deutsche Gesellschaft fUr Angewandte Datenverarbeitung und Automation in der Medizin (GADAM) - Gesellschaft fUr Informatik (GI) - Nachrichtentechnische Gesellschaft (NTG). Die DAGM fordert den Erfahrungsaustausch auf dem Gebiet der Mustererkennung und ist Mitglied der International Association for Pattern Recognition (IAPR). Die verantwortungsvolle Arbeit des Programmausschusses wurde unter Vorsitz von Prof. Hertel, Berlin, von folgenden Herren geleistet: Dr. Dreher, Bonn; Prof. Fercher, Essen; Dr. Kossel, Wetzlar; Prof. Lanzl, Oberpfaffenhofen; Dipl.lng. Platzer, MUnchen; Dr.-Ing. Poppl, MUnchen; Dr. Pretschner, Hannover; Prof. Schmidt, Oberkochen; Dr. Triendl, Oberpfaffenhofen; Dr. Walter, MUnchen; Prof. Winkler, Berlin. Unser ganz besonderer Dank gilt dem SchriftfUhrer der Deutschen Gesellschaft fUr Angewandte Optik, Herrn PreuB, Wetzlar, insbesondere fUr seine Mitarbeit im ProgrammausschuB und fUr die Drucklegung des gemeinsamen Tagungsprogramms. Wir wUnschen allen Teilnehmern, daB diese gemeinsame Tagung fUr sie ein interessanter und fruchtbarer Erfahrungsaustausch wird."

Das Rahmenthema der 25. Jahrestagung der Deutschen Gesellschaft fur Medizinische DOkumentation, Informatik und Statistik e. V. - Nachsorge und Krankheitsverlaufsanalyse - hat einen engen Bezug zu aktuellen Problemen des Gesundheitswesens. Insbesondere in Kliniken, in denen schwere Erkrankungen mit modernen erfolgversprechenden Massnahmen be- handelt werden, wird die Notwendigkeit einer systematischen weiteren Uberwachung dieser Patienten immer dringlicher erachtet. Die Beob- achtung des weiteren Schicksals, die arztliche Betreuung und die Bewertung der zeitlichen Verlaufe ergeben Ansatzpunkte fur die weitere Verbesserung der Therapie. Diese Aufgabe lasst sich nur be- waltigen, wenn die dabei auftretenden Probleme der planvollen Doku- mentation, der Informationsubermittlung, der Datenspeicherung und der statistischen Auswertung von den Vertretern unseres Faches aktiv in Angriff genommen werden. Nach 25 Jahren einer sturmischen techno- logischen und Methodenentwicklung ist unsere junge medizinische Dis- ziplin in der Lage - wenn die erforderliche apparative und perso- nelle Ausstattung zur Verfugung steht -, die Probleme der Nachsorge und Krankheitsverlaufsanalyse in Zusammenarbeit mit Klinikern und Allgemeinmedizinern wirksam zu bearbeiten und Ergebnisse zu zeitigen, die fur den Arzt relevant sind. Die Tagung soll dazu Anregungen ver- mitteln und Losungswege aufzeigen. Funf Workshops und apparative, organisatorische und methodische Pro- bleme runden den Bezug unserer Arbeit auf die Probleme der !1edizin von heute ab. Erlangen hat eine traditionelle Verbundenheit mit der technologischen Entwicklung in der Medizin. Auch heute besteht, sowohl in der Medi- zinischen wie in der Technischen Fakultat, auf deren Campus wir tragen, ein waches Interesse fur den humanen Einsatz technologischer Neuerungen, insbesondere auch der elektronischen Datenverarbeitung. L.

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.

Die automatische Auswertung von Signalen spielt in der modernen Informationstechnik eine grosse Rolle. Dieses Lehrbuch bietet, ausgehend von der Reprasentation des Signals im Merkmalraum, die Beschreibung wichtiger Klassifikationsverfahren. Dazu zahlen Linear- und Bayes Klassifikatoren, Supportvektormaschinen, Klassifikatoren auf der Basis von Gaussian-Mixture-Modellen und Hidden-Markov-Modellen sowie Klassenfolgenklassifikatoren.Weiterhin werden wichtige Grundlagen der Automatentheorie (Finite State Machines) sowie ausgewahlte maschinelle Lernverfahren dargestellt.Die Darstellung setzt die Verfahren zur Merkmalgewinnung voraus, die im ersten Band vermittelt wurden, so dass das Gesamtwerk eine umfassende Beschreibung der Kette darstellt, die in modernen Systemen der Informationsverarbeitung von der Signalerfassung bis hin zum Klassifikationsergebnis fuhrt.

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of "black-box" in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors' research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

Stereoscopic processes are increasingly used in virtual reality and entertainment. This technology is interesting because it allows for a quick immersion of the user, especially in terms of depth perception and relief clues. However, these processes tend to cause stress on the visual system if used over a prolonged period of time, leading some to question the cause of side effects that these systems generate in their users, such as eye fatigue. This book explores the mechanisms of depth perception with and without stereoscopy and discusses the indices which are involved in the depth perception. The author describes the techniques used to capture and retransmit stereoscopic images. The causes of eyestrain related to these images are then presented along with their consequences in the long and short term. The study of the causes of eyestrain forms the basis for an improvement in these processes in the hopes of developing mechanisms for easier virtual viewing.

This book describes various types of image patterns for image retrieval. All these patterns are texture dependent. Few image patterns such as Improved directional local extrema patterns, Local Quantized Extrema Patterns, Local Color Oppugnant Quantized Extrema Patterns and Local Mesh quantized extrema patterns are presented. Inter-relationships among the pixels of an image are used for feature extraction. In contrast to the existing patterns these patterns focus on local neighborhood of pixels to creates the feature vector. Evaluation metrics such as precision and recall are calculated after testing with standard databases i.e., Corel-1k, Corel-5k and MIT VisTex database. This book serves as a practical guide for students and researchers. -The text introduces two models of Directional local extrema patterns viz., Integration of color and directional local extrema patterns Integration of Gabor features and directional local extrema patterns. -Provides a framework to extract the features using quantization method -Discusses the local quantized extrema collected from two oppugnant color planes -Illustrates the mesh structure with the pixels at alternate positions.

Nel 1925, Economo e Koskinas pubblicarono l'atlante piu accurato e completo sulla citoarchitettonica della corteccia cerebrale umana mai realizzato. Una sintesi del contenuto dell'atlante venne in seguito resa disponibile in tedesco e tradotta in italiano, inglese e francese. Il valore scientifico di quest'opera e divenuto piu significativo negli ultimi vent'anni con l'avvento delle tecniche di neuroimaging funzionale, le quali consentono persino di rilevare specifici focolai di attivazione nella corteccia cerebrale umana durante l'esecuzione di compiti cognitivi. Questa riedizione in italiano e stata ampliata rispetto all'originale con l'aggiunta della mappa dei solchi e dei giri del cervello. Inoltre, e stata inclusa la tabella delle corrispondenze tra le aree individuate da Economo e Koskinas e quelle descritte da Brodmann. Il volume sara di grande interesse per tutti coloro che desiderano approfondire la relazione tra la struttura del cervello e le sue funzioni; rappresentera inoltre un utile strumento di lavoro per i professionisti che utilizzano le neuroimmagini nella loro pratica quotidiana, quali neurofisiologi, neuropsicologi, neuroradiologi, neurologi e neurochirurghi.

Edited by a panel of experts, this book fills a gap in the existing literature by comprehensively covering system, processing, and application aspects of biometrics, based on a wide variety of biometric traits. The book provides an extensive survey of biometrics theory, methods,and applications, making it an indispensable source of information for researchers, security experts, policy makers, engineers, practitioners, and graduate students. The book's wide and in-depth coverage of biometrics enables readers to build a strong, fundamental understanding of theory and methods, and provides a foundation for solutions to many of today's most interesting and challenging biometric problems. Biometric traits covered: Face, Fingerprint, Iris, Gait, Hand Geometry, Signature, Electrocardiogram (ECG), Electroencephalogram (EEG), physiological biometrics. Theory, Methods and Applications covered: Multilinear Discriminant Analysis, Neural Networks for biometrics, classifier design, biometric fusion, Event-Related Potentials, person-specific characteristic feature selection, image and video-based face, recognition/verification, near-infrared face recognition, elastic graph matching, super-resolution of facial images, multimodal solutions, 3D approaches to biometrics, facial aging models for recognition, information theory approaches to biometrics, biologically-inspired methods, biometric encryption, decision-making support in biometric systems, privacy in biometrics

Der vorliegende Band beschaftigt sich mit Mediennetzen. Vorgestellt werden die Techniken, die beim Transport von Informationen durch unterschiedliche Netze zum Einsatz kommen. Schwerpunkte bilden also die "Verpackung" der Information z.B. durch Kompressionsverfahren und der Transport digitalisierter Information. Die nachste Frage, die sich beim Transport von Information stellt, ist die nach der Mediensicherheit. Verfahren wie Digitale Wasserzeichen und Digital-Rights-Management-Systeme werden erlautert. Schliesslich werden verschiedene Multimediennetze und insbesondere Mobile Netze prasentiert."

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

This book constitutes the proceedings of the 16th Chinese Conference on Biometric Recognition, CCBR 2022, which took place in Beijing, China, in November 2022. The 70 papers presented in this volume were carefully reviewed and selected from 115 submissions. The papers cover a wide range of topics such as Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Gesture and Action Recognition; Affective Computing and Human-Computer Interface; Speaker and Speech Recognition; Gait, Iris and Other Biometrics; Multi-modal Biometric Recognition and Fusion; Quality Evaluation and Enhancement of Biometric Signals; Animal Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.

Sacred Geometry exists all around us in the natural world, from the unfurling of a rose bud to the pattern of a tortoise shell, the sub-atomic to the galactic. A pure expression of number and form, it is the language of creation and navigates the unseen dimensions beyond our three-dimensional reality. Since its discovery, humans have found many ways - stone circles, mandalas, labyrinths, temples- to call upon this universal law as a way of raising consciousness and communicating with a divine source. By becoming aware of the dots and lines that build the world around you, Sacred Geometry will teach you how to bring this mystical knowledge into your daily practice.

This book constitutes the refereed proceedings of the 14th National Conference on Man-Machine Speech Communication, NCMMSC 2017, held in Lianyungang, China, in October 2017. The 13 revised full papers presented were carefully reviewed and selected from 39 submissions. The papers address issues such as challenging issues in speech recognition and enhancement, speaker and language recognition, speech synthesis, corpus and phonetic in speech technology, speech generation, speech analyzing and modelling, speech processing of ethnic minorities, speech emotion recognition and audio signal processing.

A new approach to the issue of data quality in pattern recognition Detailing foundational concepts before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining. Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step instructions help ensure successful implementation of new processes. By positioning data quality as a factor to be dealt with rather than overcome, the framework provided serves as a valuable, versatile tool in the analysis arsenal. For decades, practical need has inspired intense theoretical and applied research into pattern recognition for numerous and diverse applications. Throughout, the limiting factor and perpetual problem has been data--its sheer diversity, abundance, and variable quality presents the central challenge to pattern recognition innovation. Pattern Recognition: A Quality of Data Perspective repositions that challenge from a hurdle to a given, and presents a new framework for comprehensive data analysis that is designed specifically to accommodate problem data. Designed as both a practical manual and a discussion about the most useful elements of pattern recognition innovation, this book: Details fundamental pattern recognition concepts, including feature space construction, classifiers, rejection, and evaluation Provides a systematic examination of the concepts, design methodology, and algorithms involved in pattern recognition Includes numerous experiments, detailed schemes, and more advanced problems that reinforce complex concepts Acts as a self-contained primer toward advanced solutions, with detailed background and step-by-step processes Introduces the concept of granules and provides a framework for granular computing Pattern recognition plays a pivotal role in data analysis and data mining, fields which are themselves being applied in an expanding sphere of utility. By facing the data quality issue head-on, this book provides students, practitioners, and researchers with a clear way forward amidst the ever-expanding data supply.

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control. Face Detection and Recognition: Theory and Practice elaborates on andexplains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then: Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB (R)/PYTHON) and hardware implementation strategies with code examples Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.

Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques.

This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples.

"Statistical Pattern Recognition," 3rd Edition: Provides a self-contained introduction to statistical pattern recognition.Includes new material presenting the analysis of complex networks.Introduces readers to methods for Bayesian density estimation.Presents descriptions of new applications in biometrics, security, finance and condition monitoring.Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applicationsDescribes mathematically the range of statistical pattern recognition techniques.Presents a variety of exercises including more extensive computer projects.

The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. "Statistical Pattern Recognition" is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields.

www.wiley.com/go/statistical_pattern_recognition