This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models. All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods. The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling. Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied. MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
Virtual machines are rapidly becoming an essential element in providing system security, flexibility, cross-platform compatibility, reliability, and resource efficiency. Designed to solve problems in combining and using major computer system components, virtual machine technologies are important to a number of disciplines, including operating systems, programming languages, and computer architecture. For example, at the process level, virtualizing technologies support dynamic program translation and platform-independent network computing. At the system level, they support multiple operating system environments on the same hardware platform and in servers. Historically, individual virtual machine techniques have been developed within the specific disciplines that employ them (in some cases they aren?t even referred to as ?virtual machines?), making it difficult to see their common underlying relationships in a cohesive way. In this text, Smith and Nair take a new approach by examining virtual machines as a unified discipline. Pulling together cross-cutting technologies allows virtual machine implementations to be studied and engineered in a well-structured manner. Topics include instruction set emulation, dynamic program translation and optimization, high level virtual machines (including Java and CLI), and system virtual machines for both single-user systems and servers.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
A few short decades ago we were informed by the smooth signals of analog television, radio, and vinyl discs; communicated with our analog telephones; and even computed with analog computers. Today our world is digital, built with zeros and ones. Why did this revolution occur? The Discrete Charm of the Machine explains, in an engaging and accessible manner, the varied physical and logical reasons behind this radical transformation. The spark of individual genius shines through this story of innovation: the stored program of Jacquard´s loom; the logical branching of Charles Babbage; Alan Turing´s brilliant abstraction of the discrete machine; Harry Nyquist´s foundation for digital signal processing; Claude Shannon´s breakthrough insights into the meaning of information and bandwidth; and Richard Feynman´s prescient proposals for nanotechnology and quantum computing. Ken Steiglitz follows the progression of these ideas in the building of our digital world, from the internet and artificial intelligence to the edge of the unknown. Are questions like the famous traveling salesman problem truly beyond the reach of ordinary digital computers? Can quantum computers transcend these barriers? Does a mysterious magical power reside in the analog mechanisms of the brain? Steiglitz concludes by confronting the moral and aesthetic questions raised by the development of artificial intelligence and autonomous robots.
A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information. The basic principle of metaheuristics is to sample a set of solutions which is large enough to be completely sampled. As metaheuristics make few assumptions about the optimization problem to be solved, they may be put to use in a variety of problems. Metaheuristics do not however, guarantee that a globally optimal solution can be found on some class of problems since most of them implement some form of stochastic optimization. Hence the solution found is often dependent on the set of random variables generated. By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics. As such, they are useful approaches for optimization problems. Even though the metaheuristics are robust enough to yield optimum solutions, yet they often suffer from time complexity and degenerate solutions. In an effort to alleviate these problems, scientists and researchers have come up with the hybridization of the different metaheuristic approaches by conjoining with other soft computing tools and techniques to yield failsafe solutions. In a recent advancement, quantum mechanical principles are being employed to cut down the time complexity of the metaheuristic approaches to a great extent. Thus, the hybrid metaheuristic approaches have come a long way in dealing with the real life optimization problems quite successfully. Proper and faithful analysis of digital images has been in the helm of affairs in the computer vision research community given the varied amount of uncertainty inherent in digital images. Images exhibit varied uncertainty and ambiguity of information and hence understanding an image scene is far from being a general procedure. The situation becomes even graver when the images become corrupt with noise artifacts. The applications of proper analysis of images encompass a wide range of applications which include image processing, image mining, image inpainting, video surveillance, intelligent transportation systems to name a few. One of the notable areas of research in image analysis is the estimation of age progression in human beings through analysis of wrinkles in face images, which can be further utilized for tracing unknown or missing persons. Hurdle detection is one of the common tasks in robotic vision that have been done through image processing, by identifying different type of objects in the image and then calculating the distance between robot and hurdles. Image analysis has a lot to contribute in this direction. Processing of color images takes the problem of image analysis to a new dimension. Apart from processing and analysis of the color gamut which involves a lot of computational overhead, the problem also involves analysis of the varied amount of uncertainty exhibited by the color images. A video is a very fast movement of pictures. Video analysis as a part of image analysis focuses on Shot Boundary Detection (SBD), dissolve detection, detection of gradual transitions and detection of fade ins/outs. Recent trends in research on image analysis rely heavily on pose and gesture analysis. Typical applications include human-machine interaction, behavior analysis, video surveillance, annotation, search and retrieval, motion capture for the entertainment industry and interactive web-based applications. Real-time video analysis algorithms mainly focus on hand and head tracking and gesture analysis. A faithful gesture recognition algorithm can be implemented with techniques borrowed from computer vision and image processing. The evolution of the functional Magnetic Resonance Imaging (fMRI) has led to proper analysis of the study mechanisms in the brain. Several statistic
As aplicações de mineração de dados e textos oriundos da Internet têm sido alvo de recentes pesquisas. E, em todos os casos, as tarefas de mineração de dados necessitam trabalhar sobre dados limpos, consistentes e integrados para obter os melhores resultados. Sendo assim, ambientes de Data Warehouse são uma valiosa fonte de dados limpos e integrados para as aplicações de mineração. A tecnologia de Data Warehouse tem evoluído no sentido de recuperar e tratar dados provenientes da Web. Em particular, os sites de notícias são fontes ricas em textos, que podem compor um corpus linguístico. Inserindo o corpus em um ambiente de Data Warehouse, as aplicações poderão tirar proveito da flexibilidade que um modelo multidimensional e as operações OLAP fornecem. Dentre as vantagens estão a navegação pelos dados, a seleção da parte dos dados considerados relevantes, a análise dos dados em diferentes níveis de abstração, e a agregação, desagregação, rotação e filtragem sobre qualquer conjunto de dados. Este trabalho apresenta o ambiente de Data Warehouse Newsminer, que fornece um conjunto de textos consistente e limpo, na forma de um corpus multidimensional.
Microprocessors are regarded as one of the most important devices in our everyday machines called computers. Before we start, we need to understand what exactly microprocessors are and their appropriate implementations. In this book we explain all details and architecture and types of Microprocessor.
Turing´s famous 1936 paper introduced a formal definition of a computing machine, a Turing machine. This model led to both the development of actual computers and to computability theory, the study of what machines can and cannot compute. This book presents classical computability theory from Turing and Post to current results and methods, and their use in studying the information content of algebraic structures, models, and their relation to Peano arithmetic. The author presents the subject as an art to be practiced, and an art in the aesthetic sense of inherent beauty which all mathematicians recognize in their subject. Part I gives a thorough development of the foundations of computability, from the definition of Turing machines up to finite injury priority arguments. Key topics include relative computability, and computably enumerable sets, those which can be effectively listed but not necessarily effectively decided, such as the theorems of Peano arithmetic. Part II includes the study of computably open and closed sets of reals and basis and nonbasis theorems for effectively closed sets. Part III covers minimal Turing degrees. Part IV is an introduction to games and their use in proving theorems. Finally, Part V offers a short history of computability theory. The author has honed the content over decades according to feedback from students, lecturers, and researchers around the world. Most chapters include exercises, and the material is carefully structured according to importance and difficulty. The book is suitable for advanced undergraduate and graduate students in computer science and mathematics and researchers engaged with computability and mathematical logic.
Machine learning methods majorly comprise image processing and soft computing methods and are mainly responsible for automation. Wheat production is influenced by assorted varying factors. A basic requisite for best production is the seed. Sorting or grading of agricultural products influenced by computer varies product wise and even product variety wise which changes region wise. Grading for new varieties released by the agricultural scientist is the major concerned as new varieties are produced by crossing existing varieties and for these proven optimized machine learning algorithms may give an adverse result. This book highlights various applications of image processing in agriculture and introduces a machine learning algorithm capable of classifying major 5 wheat cultivars (TRITICUM - DURUM: GDW 1255 (released in 2013 by ICAR) & TRITICUM - AESTVIUM: GW 273, GW 322, GW 496 & LOK 1) cultivated in Gujarat region. Experimental data consist of 11 traits comprised of shape, color & morphological characteristics. After applying feature selection algorithm,5 traits were considered & LM backpropagation was used to classify above wheat cultivars which ensued to more than 90.0% accuracy.
Data Scientisten (m/w) sind derzeit auf dem Jobmarkt heißbegehrt. In Amerika sind erfahrene Data Scientisten so beliebt wie eine Getränkebude in der Wüste. Aber auch in Deutschland ist eine steigende Nachfrage nach diesem Skillprofil erkennbar. Immer mehr Unternehmen bauen ´´Analytics´´-Abteilungen auf bzw. aus und suchen entsprechende Mitarbeiter. Nur: was macht eigentlich ein Data Scientist? Irgendetwas mit künstlicher Intelligenz, Machine Learning, Data-Mining, Python-Programmierung und Big Data. So genau weiß es eigentlich niemand ... Das Buch ist eine Einführung und Übersicht über das weitumfassende Themengebiet Data Science. Es werden die Datenquellen (Datenbanken, Data-Warehouse, Hadoop etc.) und die Softwareprodukte für die Datenanalyse vorgestellt (Data-Science-Plattformen, ML Bibliotheken). Die wichtigsten Verfahren des Machine Learnings werden ebenso behandelt wie beispielhafte Anwendungsfälle aus verschiedenen Branchen.