Python data mining and machine learning in action

Succinct, lively, and affordable, ESSENTIAL CINEMA: AN INTRODUCTION TO FILM ANALYSIS vividly illustrates principles in action as it helps you develop effective skills in close analysis. The book is pa

Packed with real-world illustrations and the latest data available, BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES, 7e demystifies and fully explains statistics in a lively, reader-friendly format. The

Packed with real-world illustrations and the latest data available, BASIC STATISTICS FOR THE BEHAVIORAL SCIENCES, 7e demystifies and fully explains statistics in a lively, reader-friendly format. The

"Preface Support vector machines (SVMs), which were introduced by Vapnik in the early 1990s, are proved effective and promising techniques for data mining. SVMs have recently been breakthroughs in adv

The 1990s saw a paradigm change in the use of corpus-driven methods in NLP. In the field of multilingual NLP (such as machine translation and terminology mining) this implied the use of parallel corpo

Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal proce

The relationship between memory and language and the topic of bilingualism are important areas of research in both psychology and linguistics and are grounded in cognitive and linguistic paradigms, theories and experimentation. This volume provides an integrated theoretical/real-world approach to second language learning, use and processing from a cognitive perspective. A strong international and interdisciplinary team of contributors present the results of various explorations into bilingual language processing, from recent advances in studies on bilingual memory to studies on the role of the brain in language processing and language forgetting. This is a strong yet balanced combination of theoretical/overview contributions and accounts of novel, original, empirical studies which will educate readers on the relationship between theory, cognitive experimentation and data and their role in understanding language learning and practice.

Modern blockbuster movies seamlessly introduce impossible characters and action into real-world settings using digital visual effects. These effects are made possible by research from the field of computer vision, the study of how to automatically understand images. Computer Vision for Visual Effects will educate students, engineers and researchers about the fundamental computer vision principles and state-of-the-art algorithms used to create cutting-edge visual effects for movies and television. The author describes classical computer vision algorithms used on a regular basis in Hollywood (such as blue screen matting, structure from motion, optical flow and feature tracking) and exciting recent developments that form the basis for future effects (such as natural image matting, multi-image compositing, image retargeting and view synthesis). He also discusses the technologies behind motion capture and three-dimensional data acquisition. More than 200 original images demonstrating princi

Graph Embedding for Pattern Recognition covers theory methods, computation, and applications widely used in statistics, machine learning, image processing, and computer vision. This book presents the

Thousands of years in the future the divide between humanity and technology has become nearly unrecognizable. Each thought, each action is logged, coded, backed up. Data is as easily exchanged through

The ability to manage change successfully is an essential part of business. It is a skill that is much valued by employers, and it is therefore one of the most commonly delivered courses. This book helps you to understand three key activities for managing change: diagnosing, explaining and enacting. Both practical and action-oriented, it gives students and managers the tools they need to deal with the messy reality of change. It combines theory and diagnostic tools with practical examples that focus on actions and outcomes. It also includes short vignettes and longer cases, from a range of international contexts, for classroom study or for use on distance learning courses. Managing Change is written for advanced undergraduates and graduate students taking modules on change management, strategy and organizations. Its class-tested approach has been successfully delivered in a wide variety of settings, including over fifty executive short courses with FTSE-listed businesses.

In this call for action, Nancy Kindelan argues that theatre's signature pedagogy of engagement plays a significant role in helping all undergraduates achieve the aims and learning goals of a contempor

He's big, he's strong, he's green—he's the Incredible Hulk! Boys ages 2-5 will love learning all about the Hulk's awesome superpowers and seeing him smash his most fearsome foes in this action-packed

Alexander von Humboldt (1769–1859), 'the greatest scientific traveller who ever lived' according to Darwin, made groundbreaking contributions to the fields of geography, oceanography, climatology and ecology. In 1804, he returned from a five-year exploration of Latin America with an incredible wealth of specimens and data which provided the foundations for his theories on the natural order. He expounds them in this book, which was printed in German in 1808 before being translated by the geographer Jean-Baptiste Benoît Eyriès (1767–1846) and published in French in 1828. Humboldt does more than provide descriptions of the great features and phenomena of the Earth, ranging from the geological character of immense plains and steppes to the structure and action of volcanoes. He combines a rigorous scientific approach with his emotional and aesthetic responses to the natural world, thereby constructing a true 'philosophy of nature'.

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual pro

What is knowledge and how is it represented? This book focuses on the idea of formalising knowledge as relations, interpreting knowledge represented in databases or logic programs as relational data and discovering new knowledge by identifying hidden and defining new relations. After a brief introduction to representational issues, the author develops a relational language for abstract machine learning problems. He then uses this language to discuss traditional methods such as clustering and decision tree induction, before moving onto two previously underestimated topics that are just coming to the fore: rough set data analysis and inductive logic programming. Its clear and precise presentation is ideal for undergraduate computer science students. The book will also interest those who study artificial intelligence or machine learning at the graduate level. Exercises are provided and each concept is introduced using the same example domain, making it easier to compare the individual pro

Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysi

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old"

This collection of twenty-one articles on machine intelligence showcases current scholarship in a variety of domains relating to the development of learning computers, advanced algorithms, and automat

This book introduces in a systematic manner a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important and varied applications in medical diagnostics, image analysis, and machine vision. An early chapter of examples establishes the effectiveness of the new methods and demonstrates how they outperform their parametric counterparts. Inference is developed for both intrinsic and extrinsic Fréchet means of probability distributions on manifolds, then applied to shape spaces defined as orbits of landmarks under a Lie group of transformations - in particular, similarity, reflection similarity, affine and projective transformations. In addition, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for statisticians who analyze manifold data and wish to develop their own methodology, this book is also of interest to probabilists, mathematicia

The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field.

Ordered sets are ubiquitous in mathematics and have significant applications in computer science, statistics, biology and the social sciences. As the first book to deal exclusively with finite ordered sets, this book will be welcomed by graduate students and researchers in all of these areas. Beginning with definitions of key concepts and fundamental results (Dilworth's and Sperner's theorem, interval and semiorders, Galois connection, duality with distributive lattices, coding and dimension theory), the authors then present applications of these structures in fields such as preference modelling and aggregation, operational research and management, cluster and concept analysis, and data mining. Exercises are included at the end of each chapter with helpful hints provided for some of the most difficult examples. The authors also point to further topics of ongoing research.

"The Information Plus Reference Series" provides statistical data on 32 of today's most controversial and most studied social issues. Each Information Plus title is a compilation of current and histor

As the power of computing has grown over the past few decades, the field of machinelearning has advanced rapidly in both theory and practice. Machine learning methods are usuallybased on the assumptio

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks.

Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Itô process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum–Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a val

"Preface this text is a summary of techniques of data analysis and modeling that the authors have encountered and used in our two-decades experience of practicing the art of applied data mining across

Discover how real-world policy issues, decisions, and applications impact you every day with the practical insights and accessible explanations in Hall/Lieberman's ECONOMICS. Filled with economic data

“Practical Applications of Evolutionary Computation to Financial Engineering” presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionar

Working with data and databases in C# certainly can be daunting if you’re coming from VB6, VBA, or Access. With this hands-on guide, you’ll shorten the learning curve considerably as you master access

A basic understanding of multiple regression is helpful in carrying out good business practices-- specifically in the areas of demand management and data analysis. This book on correlation and regress

In 1860, Charles Francis Hall (1821–71), the American explorer, embarked on the first of two voyages to the Canadian Arctic region aimed at investigating the fate of Sir John Franklin's lost expedition of 1847. During his time in the Arctic, Hall lived amongst the Inuit community, learning their language and embracing their everyday life. First published in 1864, Hall's two-volume work remains of great interest to anthropologists, sociologists and geographers. His eye-witness accounts of the indigenous people's dwellings, hunting pursuits, birth and death rites, transportation, interpersonal relationships, and survival strategies in severe weather conditions provide an insight into Inuit culture in the nineteenth century. In Volume 2 he tells of his discovery in Frobisher Bay of artefacts from Martin Frobisher's sixteenth-century mining venture; the survival of these relics, together with his understanding of Inuit memory systems, convinces him that traces of Franklin and his crew may

Standards provide learning targets for students and the result of assessments help make data-driven instructional decisions. In this anthology education experts address the following topics in the con

A variety of exercises that allow students to apply what they have learned in the text. These include psychological tests, data to calculate adverse impact, and case studies.

Poetry. In the age of data mining and identity theft, Dana Teen Lomax's DISCLOSURE has been called "the new memoir." Recently anthologized in Against Expression: An Anthology of Cultural Writing (Nort

This first book on greedy approximation gives a systematic presentation of the fundamental results. It also contains an introduction to two hot topics in numerical mathematics: learning theory and compressed sensing. Nonlinear approximation is becoming increasingly important, especially since two types are frequently employed in applications: adaptive methods are used in PDE solvers, while m-term approximation is used in image/signal/data processing, as well as in the design of neural networks. The fundamental question of nonlinear approximation is how to devise good constructive methods (algorithms) and recent results have established that greedy type algorithms may be the solution. The author has drawn on his own teaching experience to write a book ideally suited to graduate courses. The reader does not require a broad background to understand the material. Important open problems are included to give students and professionals alike ideas for further research.

Machine learning builds models of the world using training data from the application domain and prior knowledge about the problem. The models are later applied to future data in order to estimate the

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided fro

Presenting a coherent synthesis of lithosphere studies, this book covers a range of geophysical methods (seismic reflection, refraction, and receiver function methods; elastic and anelastic seismic tomography; electromagnetic and magnetotelluric methods; thermal, gravity and rheological models), complemented by petrologic and laboratory data on rock properties. It also provides a critical discussion of the uncertainties, assumptions, and resolution issues that are inherent in the different methods and models of the lithosphere. Multidisciplinary in scope, global in geographical extent, and covering a wide variety of tectonics settings across 3.5 billion years of Earth history, this book presents a comprehensive overview of lithospheric structure and evolution. It is a core reference for researchers and advanced students in geophysics, geodynamics, tectonics, petrology, and geochemistry, and for petroleum and mining industry professionals.

Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication. The book “Modelling, Learning and