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Algorithms for Sparsity-Constrained Optimization (Springer Theses)

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This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.
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Introduction to Probability and Statistics Using R
Introduction to Probability and Statistics Using R
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three... More > semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors....
Mathematical Logic for Computer Science: Third Edition
Mathematical Logic for Computer Science: Third Edition
This book approaches mathematics based on the needs of computer science students, teaching logic by means of the method of semantic tableaux. The third edition is entirely rewritten, and includes new chapters on SAT solvers and model checking....
Nominal Sets: Names and Symmetry in Computer Science (Cambridge Tracts in Theoretical Computer Science)
Nominal Sets: Names and Symmetry in Computer Science (Cambridge Tracts in Theoretical Computer Science)
Nominal sets provide a promising new mathematical analysis of names in formal languages based upon symmetry, with many applications to the syntax and semantics of programming language constructs that involve binding, or localising names. Part I provides an introduction to the basic theory of nominal sets. In Part II, the author surveys some of...

Applied Data Mining
Applied Data Mining

Data mining has witnessed substantial advances in recent decades. New research questions and practical challenges have arisen from emerging areas and applications within the various fields closely related to human daily life, e.g. social media and social networking. This book aims to bridge the gap between traditional data mining and...

Separable Type Representations of Matrices and Fast Algorithms: Volume 1 Basics. Completion Problems. Multiplication and Inversion Algorithms (Operator Theory: Advances and Applications)
Separable Type Representations of Matrices and Fast Algorithms: Volume 1 Basics. Completion Problems. Multiplication and Inversion Algorithms (Operator Theory: Advances and Applications)
This two-volume work presents a systematic theoretical and computational study of several types of generalizations of separable matrices. The main attention is paid to fast algorithms (many of linear complexity) for matrices in semiseparable, quasiseparable, band and companion form. The work is focused on algorithms of multiplication, inversion...
Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!)
Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!)
Data Mining and Anlaytics are the foundation technologies for the new knowledge based world where we build models from data and databases to understand and explore our world. Data mining can improve our business, improve our government, and improve our life and with the right tools, any one can begin to explore this new technology, on the path...
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