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The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining.

Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine learning

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This book introduces the so-called ""stable factorization approach"" to the synthesis of feedback controllers for linear control systems. The key to this approach is to view the multi-input, multi-output (MIMO) plant for which one wishes to design a controller as a matrix over the fraction field F associated with a commutative ring with identity, denoted by R, which also has no divisors of zero. In this setting, the set of single-input, single-output (SISO) stable control systems is precisely the ring R, while the set of stable MIMO control systems is the set of matrices whose elements all belong to R. The set of unstable, meaning not necessarily stable, control systems is then taken to be the field of fractions F associated with R in the SISO case, and the set of matrices with elements in F in the MIMO case. The central notion introduced in the book is that, in most situations of practical interest, every matrix P whose elements belong to F can be ""factored"" as a ""ratio"" of two matrices N,D whose elements belong to R, in such a way that N,D are coprime. In the familiar case where the ring R corresponds to the set of bounded-input, bounded-output (BIBO)-stable rational transfer functions, coprimeness is equivalent to two functions not having any common zeros in the closed right half-plane including infinity. However, the notion of coprimeness extends readily to discrete-time systems, distributed-parameter systems in both the continuous- as well as discrete-time domains, and to multi-dimensional systems. Thus the stable factorization approach enables one to capture all these situations within a common framework. The key result in the stable factorization approach is the parametrization of all controllers that stabilize a given plant. It is shown that the set of all stabilizing controllers can be parametrized by a single parameter R, whose elements all belong to R. Moreover, every transfer matrix in the closed-loop system is an affine function of the design parameter R. Thus problems of reliable stabilization, disturbance rejection, robust stabilization etc. can all be formulated in terms of choosing an appropriate R. This is a reprint of the book Control System Synthesis: A Factorization Approach originally published by M.I.T. Press in 1985. Table of Contents: Filtering and Sensitivity Minimization / Robustness / Extensions to General Settings.

Data mining. --- Statistics. --- Data Mining and Knowledge Discovery.

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Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizatio

Information Technology --- General and Others --- Data mining. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- Inteligencia del negocio (40154309) --- Bibliografía recomendada

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For decades experiments conducted on space stations like MIR and the ISS have been gathering data in many fields of research in the natural sciences, medicine and engineering. The European Union-sponsored ULISSE project focused on exploring the wealth of unique experimental data provided by revealing raw and metadata from these studies via an Internet Portal. This book complements the portal. It serves as a handbook of space experiments and describes the various types of experimental infrastructure areas of research in the life and physical sciences and technology space missions that hosted scientific experiments the types and structures of the data produced and how one can access the data through ULISSE for further research. The book provides an overview of the wealth of space experiment data that can be used for additional research and will inspire academics (e.g. those looking for topics for their PhD thesis) and research departments in companies for their continued development.

Space sciences --- Astronomy & Astrophysics --- Physical Sciences & Mathematics --- Astrophysics --- Astronomy - General --- Astronomy. --- Physics. --- Earth sciences. --- Medicine. --- Data mining. --- Life sciences. --- Astrophysics. --- Cosmology. --- Aerospace engineering. --- Astronautics. --- Astronomy, Astrophysics and Cosmology. --- Aerospace Technology and Astronautics. --- Life Sciences, general. --- Medicine/Public Health, general. --- Earth Sciences, general. --- Data Mining and Knowledge Discovery. --- Physical sciences --- Geography. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Cosmography --- Earth sciences --- World history --- Clinical sciences --- Medical profession --- Human biology --- Life sciences --- Medical sciences --- Pathology --- Physicians --- Biosciences --- Sciences, Life --- Science --- Aeronautics --- Astrodynamics --- Space flight --- Space vehicles --- Health Workforce --- Geosciences --- Environmental sciences --- Aeronautical engineering --- Astronautics --- Engineering --- Astronomical physics --- Astronomy --- Cosmic physics --- Physics

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Statistics and hypothesis testing are routinely used in areas (such as linguistics) that are traditionally not mathematically intensive. In such fields, when faced with experimental data, many students and researchers tend to rely on commercial packages to carry out statistical data analysis, often without understanding the logic of the statistical tests they rely on. As a consequence, results are often misinterpreted, and users have difficulty in flexibly applying techniques relevant to their own research — they use whatever they happen to have learned. A simple solution is to teach the fundamental ideas of statistical hypothesis testing without using too much mathematics. This book provides a non-mathematical, simulation-based introduction to basic statistical concepts and encourages readers to try out the simulations themselves using the source code and data provided (the freely available programming language R is used throughout). Since the code presented in the text almost always requires the use of previously introduced programming constructs, diligent students also acquire basic programming abilities in R. The book is intended for advanced undergraduate and graduate students in any discipline, although the focus is on linguistics, psychology, and cognitive science. It is designed for self-instruction, but it can also be used as a textbook for a first course on statistics. Earlier versions of the book have been used in undergraduate and graduate courses in Europe and the US.   ”Vasishth and Broe have written an attractive introduction to the foundations of statistics. It is concise, surprisingly comprehensive, self-contained and yet quite accessible. Highly recommended.” Harald Baayen, Professor of Linguistics, University of Alberta, Canada   ”By using the text students not only learn to do the specific things outlined in the book, they also gain a skill set that empowers them to explore new areas that lie beyond the book’s coverage.” Colin Phillips, Professor of Linguistics, University of Maryland, USA.

Statistics --- Mathematical statistics. --- Mathematics. --- Data mining. --- Applied mathematics. --- Engineering mathematics. --- Psycholinguistics. --- Mathematics --- Psychology. --- Applications of Mathematics. --- Data Mining and Knowledge Discovery. --- Mathematics Education. --- Psychology, general. --- Study and teaching. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Philosophy (General). --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Language, Psychology of --- Language and languages --- Psychology of language --- Speech --- Linguistics --- Psychology --- Thought and thinking --- Math --- Science --- Psychological aspects --- Mathematical statistics --- Computer simulation. --- Simulation methods. --- Mathematics—Study and teaching . --- Behavioral sciences --- Mental philosophy --- Mind --- Science, Mental --- Human biology --- Philosophy --- Soul --- Mental health --- Engineering --- Engineering analysis --- Mathematical analysis