Download or read book Matrix Methods in Data Mining and Pattern Recognition written by Lars Elden and published by SIAM. This book was released on 2007-07-12 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several very powerful numerical linear algebra techniques are available for solving problems in data mining and pattern recognition. This application-oriented book describes how modern matrix methods can be used to solve these problems, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.Matrix Methods in Data Mining and Pattern Recognition is divided into three parts. Part I gives a short introduction to a few application areas before presenting linear algebra concepts and matrix decompositions that students can use in problem-solving environments such as MATLAB®. Some mathematical proofs that emphasize the existence and properties of the matrix decompositions are included. In Part II, linear algebra techniques are applied to data mining problems. Part III is a brief introduction to eigenvalue and singular value algorithms. The applications discussed by the author are: classification of handwritten digits, text mining, text summarization, pagerank computations related to the GoogleÔ search engine, and face recognition. Exercises and computer assignments are available on a Web page that supplements the book.Audience The book is intended for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course. Graduate students in various data mining and pattern recognition areas who need an introduction to linear algebra techniques will also find the book useful.Contents Preface; Part I: Linear Algebra Concepts and Matrix Decompositions. Chapter 1: Vectors and Matrices in Data Mining and Pattern Recognition; Chapter 2: Vectors and Matrices; Chapter 3: Linear Systems and Least Squares; Chapter 4: Orthogonality; Chapter 5: QR Decomposition; Chapter 6: Singular Value Decomposition; Chapter 7: Reduced-Rank Least Squares Models; Chapter 8: Tensor Decomposition; Chapter 9: Clustering and Nonnegative Matrix Factorization; Part II: Data Mining Applications. Chapter 10: Classification of Handwritten Digits; Chapter 11: Text Mining; Chapter 12: Page Ranking for a Web Search Engine; Chapter 13: Automatic Key Word and Key Sentence Extraction; Chapter 14: Face Recognition Using Tensor SVD. Part III: Computing the Matrix Decompositions. Chapter 15: Computing Eigenvalues and Singular Values; Bibliography; Index.

Download or read book Matrix Methods in Data Mining and Pattern Recognition written by Lars Elden and published by SIAM. This book was released on 2007-01-01 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This application-oriented book describes how modern matrix methods can be used to solve problems in data mining and pattern recognition, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application.

Download or read book Matrix Methods in Data Mining and Pattern Recognition Second Edition written by Lars Elden and published by SIAM. This book was released on 2019-08-30 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thoroughly revised second edition provides an updated treatment of numerical linear algebra techniques for solving problems in data mining and pattern recognition. Adopting an application-oriented approach, the author introduces matrix theory and decompositions, describes how modern matrix methods can be applied in real life scenarios, and provides a set of tools that students can modify for a particular application. Building on material from the first edition, the author discusses basic graph concepts and their matrix counterparts. He introduces the graph Laplacian and properties of its eigenvectors needed in spectral partitioning and describes spectral graph partitioning applied to social networks and text classification. Examples are included to help readers visualize the results. This new edition also presents matrix-based methods that underlie many of the algorithms used for big data. The book provides a solid foundation to further explore related topics and presents applications such as classification of handwritten digits, text mining, text summarization, PageRank computations related to the Google search engine, and facial recognition. Exercises and computer assignments are available on a Web page that supplements the book. This book is primarily for undergraduate students who have previously taken an introductory scientific computing/numerical analysis course and graduate students in data mining and pattern recognition areas who need an introduction to linear algebra techniques.

Download or read book Understanding Complex Datasets written by David Skillicorn and published by CRC Press. This book was released on 2007-05-17 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book

Download or read book Modern Structural Analysis written by Anthony E. Armenàkas and published by McGraw-Hill Companies. This book was released on 1991 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: This companion to the previously published book [BO]Classical Structural Analysis[BX], also by the same author, focuses on advanced structural analysis using matrix methods for the element method of design calculations. With this method, the structural properties of each structural member (or element) taken together, of an entire structure, are used to calculate load behaviour and construction needs of a whole building or other structure. The matrix method is particularly suited to computer methods that must employ thousands of reiterate calculations. The book contains dozens of worked-out problems and design exercises, as well as an actual computer program at the end of the book for matrix method calculations.

Download or read book Machine Interpretation of Patterns written by Rajat K. De and published by World Scientific. This book was released on 2010 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Combining information with a Bayesian multi-class multi-kernel pattern recognition machine / T. Damoulas and M.A. Girolami -- 2. Image quality assessment based on weighted perceptual features / D.V. Rao and L.P. Reddy -- 3. Quasi-reversible two-dimension fractional differentiation for image entropy reduction / A. Nakib [und weitere] -- 4. Parallel genetic algorithm based clustering for object and background classification / P. Kanungo, P.K. Nanda and A. Ghosh -- 5. Bipolar fuzzy spatial information : first operations in the mathematical morphology setting / I. Bloch -- 6. Approaches to intelligent information retrieval / G. Pasi -- 7. Retrieval of on-line signatures / H.N. Prakash and D.S. Guru -- 8. A two stage recognition scheme for offline handwritten Devanagari Words / B. Shaw and S.K. Parui -- 9. Fall detection from a video in the presence of multiple persons / V. Vishwakarma, S. Sural and C. Mandal -- 10. Fusion of GIS and SAR statistical features for earthquake damage mapping at the block scale / G. Trianni [und weitere] -- 11. Intelligent surveillance and Pose-invariant 2D face classification / B.C. Lovell, C. Sanderson and T. Shan -- 12. Simple machine learning approaches to safety-related systems / C. Moewes, C. Otte and R. Kruse -- 13. Nonuniform multi level crossings for signal reconstruction / N. Poojary, H. Kumar and A. Rao -- 14. Adaptive web services brokering / K.M. Gupta and D.W. Aha -- 15. Granular support vector machine based method for prediction of solubility of proteins on over expression in Escherichia Coli and breast cancer classification / P. Kumar, B.D. Kulkarni and V.K. Jayaraman

Download or read book Principles of Data Mining written by David J. Hand and published by MIT Press. This book was released on 2001-08-17 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

Download or read book Pattern Recognition and Machine Learning written by Christopher M. Bishop and published by Springer. This book was released on 2016-08-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Download or read book Data Mining written by Krzysztof J. Cios and published by Springer Science & Business Media. This book was released on 2007-10-05 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Download or read book Automatic Differentiation of Algorithms written by Andreas Griewank and published by Society for Industrial & Applied. This book was released on 1991 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Numerical Analysis.

Download or read book Evolution of Random Search Trees written by Hosam M. Mahmoud and published by Wiley-Interscience. This book was released on 1992 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: While several excellent books have been written on algorithms and their analysis, remarkably few have been dedicated to the probabilistic analysis of algorithms. This graduate text/professional reference fills that gap and brings together material that is scattered over tens of publications. Its unifying theme is the study of some classes of random search trees suitable for use as data structures with a behavior of random growth that is almost as good as balanced trees.

Download or read book Mining Imperfect Data written by Ronald K. Pearson and published by SIAM. This book was released on 2020-09-10 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them. As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python. Mining Imperfect Data: With Examples in R and Python, Second Edition presents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage). It includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them, and it provides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities. While this book is primarily for data scientists, researchers in a variety of fields—namely statistics, machine learning, physics, engineering, medicine, social sciences, economics, and business—will also find it useful.

Download or read book Pattern Classification Using Ensemble Methods written by Lior Rokach and published by World Scientific. This book was released on 2010 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms. 1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction methods -- 2. Introduction to ensemble learning. 2.1. Back to the roots. 2.2. The wisdom of crowds. 2.3. The bagging algorithm. 2.4. The boosting algorithm. 2.5. The AdaBoost algorithm. 2.6. No free lunch theorem and ensemble learning. 2.7. Bias-variance decomposition and ensemble learning. 2.8. Occam's razor and ensemble learning. 2.9. Classifier dependency. 2.10. Ensemble methods for advanced classification tasks -- 3. Ensemble classification. 3.1. Fusions methods. 3.2. Selecting classification. 3.3. Mixture of experts and meta learning -- 4. Ensemble diversity. 4.1. Overview. 4.2. Manipulating the inducer. 4.3. Manipulating the training samples. 4.4. Manipulating the target attribute representation. 4.5. Partitioning the search space. 4.6. Multi-inducers. 4.7. Measuring the diversity -- 5. Ensemble selection. 5.1. Ensemble selection. 5.2. Pre selection of the ensemble size. 5.3. Selection of the ensemble size while training. 5.4. Pruning - post selection of the ensemble size -- 6. Error correcting output codes. 6.1. Code-matrix decomposition of multiclass problems. 6.2. Type I - training an ensemble given a code-matrix. 6.3. Type II - adapting code-matrices to the multiclass problems -- 7. Evaluating ensembles of classifiers. 7.1. Generalization error. 7.2. Computational complexity. 7.3. Interpretability of the resulting ensemble. 7.4. Scalability to large datasets. 7.5. Robustness. 7.6. Stability. 7.7. Flexibility. 7.8. Usability. 7.9. Software availability. 7.10. Which ensemble method should be used?

Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2013-08-21 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Download or read book Statistical Pattern Recognition written by Andrew R. Webb and published by John Wiley & Sons. This book was released on 2003-07-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition is a very active area of study andresearch, which has seen many advances in recent years. New andemerging applications - such as data mining, web searching,multimedia data retrieval, face recognition, and cursivehandwriting recognition - require robust and efficient patternrecognition techniques. Statistical decision making and estimationare regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fullyupdated with new methods, applications and references. It providesa comprehensive introduction to this vibrant area - with materialdrawn from engineering, statistics, computer science and the socialsciences - and covers many application areas, such as databasedesign, artificial neural networks, and decision supportsystems. * Provides a self-contained introduction to statistical patternrecognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vectormachines, and unsupervised classification. * Each section concludes with a description of the applicationsthat have been addressed and with further developments of thetheory. * Includes background material on dissimilarity, parameterestimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions tomore lengthy projects. The book is aimed primarily at senior undergraduate and graduatestudents studying statistical pattern recognition, patternprocessing, neural networks, and data mining, in both statisticsand engineering departments. It is also an excellent source ofreference for technical professionals working in advancedinformation development environments. For further information on the techniques and applicationsdiscussed in this book please visit ahref="http://www.statistical-pattern-recognition.net/"www.statistical-pattern-recognition.net/a

Download or read book Network Analysis and Synthesis written by Louis Weinberg and published by . This book was released on 1975 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear Algebra and Learning from Data written by Gilbert Strang and published by Wellesley-Cambridge Press. This book was released on 2019-01-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.