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Author : Saharon Shelah
Publisher : Springer
ISBN 13 : 3540397884
Total Pages : 289 pages
Book Rating : 4.5/5 (43 download)
Download or read book Around Classification Theory of Models written by Saharon Shelah and published by Springer. This book was released on 2006-12-08 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : S. Shelah
Publisher : Elsevier
ISBN 13 : 008088024X
Total Pages : 741 pages
Book Rating : 4.0/5 (88 download)
Download or read book Classification Theory written by S. Shelah and published by Elsevier. This book was released on 1990-12-06 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this research monograph, the author's work on classification and related topics are presented. This revised edition brings the book up to date with the addition of four new chapters as well as various corrections to the 1978 text.The additional chapters X - XIII present the solution to countable first order T of what the author sees as the main test of the theory. In Chapter X the Dimensional Order Property is introduced and it is shown to be a meaningful dividing line for superstable theories. In Chapter XI there is a proof of the decomposition theorems. Chapter XII is the crux of the matter: there is proof that the negation of the assumption used in Chapter XI implies that in models of T a relation can be defined which orders a large subset of m
Author : John T. Baldwin
Publisher :
ISBN 13 : 9783662210864
Total Pages : 516 pages
Book Rating : 4.2/5 (18 download)
Download or read book Classification Theory written by John T. Baldwin and published by . This book was released on 2014-01-15 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Saharon Shelah
Publisher : Lecture Notes in Mathematics
ISBN 13 :
Total Pages : 296 pages
Book Rating : 4.F/5 ( download)
Download or read book Around Classification Theory of Models written by Saharon Shelah and published by Lecture Notes in Mathematics. This book was released on 1986-04 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Matthias von Davier
Publisher : Springer Nature
ISBN 13 : 3030055841
Total Pages : 646 pages
Book Rating : 4.0/5 (3 download)
Download or read book Handbook of Diagnostic Classification Models written by Matthias von Davier and published by Springer Nature. This book was released on 2019-10-11 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification. The handbook also offers applications and special topics and practical guidelines how to plan and conduct research studies with the help of DCMs. Commonly used models in educational measurement and psychometrics typically assume a single latent trait or at best a small number of latent variables that are aimed at describing individual differences in observed behavior. While this allows simple rankings of test takers along one or a few dimensions, it does not provide a detailed picture of strengths and weaknesses when assessing complex cognitive skills. DCMs, on the other hand, allow the evaluation of test taker performance relative to a potentially large number of skill domains. Most diagnostic models provide a binary mastery/non-mastery classification for each of the assumed test taker attributes representing these skill domains. Attribute profiles can be used for formative decisions as well as for summative purposes, for example in a multiple cut-off procedure that requires mastery on at least a certain subset of skills. The number of DCMs discussed in the literature and applied to a variety of assessment data has been increasing over the past decades, and their appeal to researchers and practitioners alike continues to grow. These models have been used in English language assessment, international large scale assessments, and for feedback for practice exams in preparation of college admission testing, just to name a few. Nowadays, technology-based assessments provide increasingly rich data on a multitude of skills and allow collection of data with respect to multiple types of behaviors. Diagnostic models can be understood as an ideal match for these types of data collections to provide more in-depth information about test taker skills and behavioral tendencies.
Author : John T. Baldwin
Publisher : Cambridge University Press
ISBN 13 : 1107168090
Total Pages : 462 pages
Book Rating : 4.1/5 (71 download)
Download or read book Fundamentals of Stability Theory written by John T. Baldwin and published by Cambridge University Press. This book was released on 2017-03-02 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces first order stability theory, organized around the spectrum problem, with complete proofs of the Vaught conjecture for ω-stable theories.
Author : S. Barry Cooper
Publisher : Cambridge University Press
ISBN 13 : 0521635500
Total Pages : 433 pages
Book Rating : 4.5/5 (216 download)
Download or read book Models and Computability written by S. Barry Cooper and published by Cambridge University Press. This book was released on 1999-06-17 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Second of two volumes providing a comprehensive guide to the current state of mathematical logic.
Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author : Mike Prest
Publisher : Cambridge University Press
ISBN 13 : 0521348331
Total Pages : 402 pages
Book Rating : 4.5/5 (213 download)
Download or read book Model Theory and Modules written by Mike Prest and published by Cambridge University Press. This book was released on 1988-02-25 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years the interplay between model theory and other branches of mathematics has led to many deep and intriguing results. In this, the first book on the topic, the theme is the interplay between model theory and the theory of modules. The book is intended to be a self-contained introduction to the subject and introduces the requisite model theory and module theory as it is needed. Dr Prest develops the basic ideas concerning what can be said about modules using the information which may be expressed in a first-order language. Later chapters discuss stability-theoretic aspects of modules, and structure and classification theorems over various types of rings and for certain classes of modules. Both algebraists and logicians will enjoy this account of an area in which algebra and model theory interact in a significant way. The book includes numerous examples and exercises and consequently will make an ideal introduction for graduate students coming to this subject for the first time.
Author : Wilfrid Hodges
Publisher : Cambridge University Press
ISBN 13 : 9780521304429
Total Pages : 810 pages
Book Rating : 4.3/5 (44 download)
Download or read book Model Theory written by Wilfrid Hodges and published by Cambridge University Press. This book was released on 1993-03-11 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model theory is concerned with the notions of definition, interpretation and structure in a very general setting, and is applied to a wide range of other areas such as set theory, geometry, algebra and computer science. This book provides an integrated introduction to model theory for graduate students.
Author : Jose Iovino
Publisher : CRC Press
ISBN 13 : 042955866X
Total Pages : 596 pages
Book Rating : 4.4/5 (295 download)
Download or read book Beyond First Order Model Theory, Volume II written by Jose Iovino and published by CRC Press. This book was released on 2023-07-03 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model theory is the meta-mathematical study of the concept of mathematical truth. After Afred Tarski coined the term Theory of Models in the early 1950’s, it rapidly became one of the central most active branches of mathematical logic. In the last few decades, ideas that originated within model theory have provided powerful tools to solve problems in a variety of areas of classical mathematics, including algebra, combinatorics, geometry, number theory, and Banach space theory and operator theory. The two volumes of Beyond First Order Model Theory present the reader with a fairly comprehensive vista, rich in width and depth, of some of the most active areas of contemporary research in model theory beyond the realm of the classical first-order viewpoint. Each chapter is intended to serve both as an introduction to a current direction in model theory and as a presentation of results that are not available elsewhere. All the articles are written so that they can be studied independently of one another. This second volume contains introductions to real-valued logic and applications, abstract elementary classes and applications, interconnections between model theory and function spaces, nonstucture theory, and model theory of second-order logic. Features A coherent introduction to current trends in model theory. Contains articles by some of the most influential logicians of the last hundred years. No other publication brings these distinguished authors together. Suitable as a reference for advanced undergraduate, postgraduates, and researchers. Material presented in the book (e.g, abstract elementary classes, first-order logics with dependent sorts, and applications of infinitary logics in set theory) is not easily accessible in the current literature. The various chapters in the book can be studied independently.
Author : Charles Bouveyron
Publisher : Cambridge University Press
ISBN 13 : 1108640591
Total Pages : 447 pages
Book Rating : 4.1/5 (86 download)
Download or read book Model-Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
Author : M Droste
Publisher : CRC Press
ISBN 13 : 1000725278
Total Pages : 516 pages
Book Rating : 4.0/5 (7 download)
Download or read book Advances in Algebra and Model Theory written by M Droste and published by CRC Press. This book was released on 2019-08-16 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contains 25 surveys in algebra and model theory, all written by leading experts in the field. The surveys are based around talks given at conferences held in Essen, 1994, and Dresden, 1995. Each contribution is written in such a way as to highlight the ideas that were discussed at the conferences, and also to stimulate open research problems in a form accessible to the whole mathematical community. The topics include field and ring theory as well as groups, ordered algebraic structure and their relationship to model theory. Several papers deal with infinite permutation groups, abelian groups, modules and their relatives and representations. Model theoretic aspects include quantifier elimination in skew fields, Hilbert's 17th problem, (aleph-0)-categorical structures and Boolean algebras. Moreover symmetry questions and automorphism groups of orders are covered. This work contains 25 surveys in algebra and model theory, each is written in such a way as to highlight the ideas that were discussed at Conferences, and also to stimulate open research problems in a form accessible to the whole mathematical community.
Author : Norman Matloff
Publisher : CRC Press
ISBN 13 : 1351645897
Total Pages : 439 pages
Book Rating : 4.3/5 (516 download)
Download or read book Statistical Regression and Classification written by Norman Matloff and published by CRC Press. This book was released on 2017-09-19 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
Author : Shan Suthaharan
Publisher : Springer
ISBN 13 : 1489976418
Total Pages : 364 pages
Book Rating : 4.4/5 (899 download)
Download or read book Machine Learning Models and Algorithms for Big Data Classification written by Shan Suthaharan and published by Springer. This book was released on 2015-10-20 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learning, to gain a quick understanding of the techniques and technologies; therefore, the theory, examples, and programs (Matlab and R) presented in this book have been simplified, hardcoded, repeated, or spaced for improvements. They provide vehicles to test and understand the complicated concepts of various topics in the field. It is expected that the readers adopt these programs to experiment with the examples, and then modify or write their own programs toward advancing their knowledge for solving more complex and challenging problems. The presentation format of this book focuses on simplicity, readability, and dependability so that both undergraduate and graduate students as well as new researchers, developers, and practitioners in this field can easily trust and grasp the concepts, and learn them effectively. It has been written to reduce the mathematical complexity and help the vast majority of readers to understand the topics and get interested in the field. This book consists of four parts, with the total of 14 chapters. The first part mainly focuses on the topics that are needed to help analyze and understand data and big data. The second part covers the topics that can explain the systems required for processing big data. The third part presents the topics required to understand and select machine learning techniques to classify big data. Finally, the fourth part concentrates on the topics that explain the scaling-up machine learning, an important solution for modern big data problems.
Author : Sue L. T. McGregor
Publisher : SAGE Publications
ISBN 13 : 1506350976
Total Pages : 880 pages
Book Rating : 4.5/5 (63 download)
Download or read book Understanding and Evaluating Research written by Sue L. T. McGregor and published by SAGE Publications. This book was released on 2017-10-25 with total page 880 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and Evaluating Research: A Critical Guide shows students how to be critical consumers of research and to appreciate the power of methodology as it shapes the research question, the use of theory in the study, the methods used, and how the outcomes are reported. The book starts with what it means to be a critical and uncritical reader of research, followed by a detailed chapter on methodology, and then proceeds to a discussion of each component of a research article as it is informed by the methodology. The book encourages readers to select an article from their discipline, learning along the way how to assess each component of the article and come to a judgment of its rigor or quality as a scholarly report.
Author : Damiano Brigo
Publisher : Springer Science & Business Media
ISBN 13 : 354034604X
Total Pages : 1016 pages
Book Rating : 4.5/5 (43 download)
Download or read book Interest Rate Models - Theory and Practice written by Damiano Brigo and published by Springer Science & Business Media. This book was released on 2007-09-26 with total page 1016 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2nd edition of this successful book has several new features. The calibration discussion of the basic LIBOR market model has been enriched considerably, with an analysis of the impact of the swaptions interpolation technique and of the exogenous instantaneous correlation on the calibration outputs. A discussion of historical estimation of the instantaneous correlation matrix and of rank reduction has been added, and a LIBOR-model consistent swaption-volatility interpolation technique has been introduced. The old sections devoted to the smile issue in the LIBOR market model have been enlarged into a new chapter. New sections on local-volatility dynamics, and on stochastic volatility models have been added, with a thorough treatment of the recently developed uncertain-volatility approach. Examples of calibrations to real market data are now considered. The fast-growing interest for hybrid products has led to a new chapter. A special focus here is devoted to the pricing of inflation-linked derivatives. The three final new chapters of this second edition are devoted to credit. Since Credit Derivatives are increasingly fundamental, and since in the reduced-form modeling framework much of the technique involved is analogous to interest-rate modeling, Credit Derivatives -- mostly Credit Default Swaps (CDS), CDS Options and Constant Maturity CDS - are discussed, building on the basic short rate-models and market models introduced earlier for the default-free market. Counterparty risk in interest rate payoff valuation is also considered, motivated by the recent Basel II framework developments.
