Kernel Methods For Machine Learning With Math And R
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Author |
: Joe Suzuki |
Publisher |
: Springer Nature |
Total Pages |
: 203 |
Release |
: 2022-05-04 |
ISBN-10 |
: 9789811903984 |
ISBN-13 |
: 9811903980 |
Rating |
: 4/5 (84 Downloads) |
Synopsis Kernel Methods for Machine Learning with Math and R by : Joe Suzuki
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book’s main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Author |
: Joe Suzuki |
Publisher |
: Springer Nature |
Total Pages |
: 216 |
Release |
: 2022-05-14 |
ISBN-10 |
: 9789811904011 |
ISBN-13 |
: 9811904014 |
Rating |
: 4/5 (11 Downloads) |
Synopsis Kernel Methods for Machine Learning with Math and Python by : Joe Suzuki
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Author |
: S. Y. Kung |
Publisher |
: Cambridge University Press |
Total Pages |
: 617 |
Release |
: 2014-04-17 |
ISBN-10 |
: 9781139867634 |
ISBN-13 |
: 1139867636 |
Rating |
: 4/5 (34 Downloads) |
Synopsis Kernel Methods and Machine Learning by : S. Y. Kung
Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
Author |
: Marc Peter Deisenroth |
Publisher |
: Cambridge University Press |
Total Pages |
: 392 |
Release |
: 2020-04-23 |
ISBN-10 |
: 9781108569323 |
ISBN-13 |
: 1108569323 |
Rating |
: 4/5 (23 Downloads) |
Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author |
: John Shawe-Taylor |
Publisher |
: Cambridge University Press |
Total Pages |
: 520 |
Release |
: 2004-06-28 |
ISBN-10 |
: 0521813972 |
ISBN-13 |
: 9780521813976 |
Rating |
: 4/5 (72 Downloads) |
Synopsis Kernel Methods for Pattern Analysis by : John Shawe-Taylor
Publisher Description
Author |
: K.P. Soman |
Publisher |
: |
Total Pages |
: 476 |
Release |
: 2011 |
ISBN-10 |
: OCLC:1027888907 |
ISBN-13 |
: |
Rating |
: 4/5 (07 Downloads) |
Synopsis Machine Learning with Svm and Other Kernel Methods by : K.P. Soman
Author |
: Bernhard Scholkopf |
Publisher |
: MIT Press |
Total Pages |
: 645 |
Release |
: 2018-06-05 |
ISBN-10 |
: 9780262536578 |
ISBN-13 |
: 0262536579 |
Rating |
: 4/5 (78 Downloads) |
Synopsis Learning with Kernels by : Bernhard Scholkopf
A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.
Author |
: Francesco Camastra |
Publisher |
: Springer |
Total Pages |
: 564 |
Release |
: 2015-07-21 |
ISBN-10 |
: 9781447167358 |
ISBN-13 |
: 144716735X |
Rating |
: 4/5 (58 Downloads) |
Synopsis Machine Learning for Audio, Image and Video Analysis by : Francesco Camastra
This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
Author |
: Dirk P. Kroese |
Publisher |
: CRC Press |
Total Pages |
: 538 |
Release |
: 2019-11-20 |
ISBN-10 |
: 9781000730777 |
ISBN-13 |
: 1000730778 |
Rating |
: 4/5 (77 Downloads) |
Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author |
: Mehryar Mohri |
Publisher |
: MIT Press |
Total Pages |
: 505 |
Release |
: 2018-12-25 |
ISBN-10 |
: 9780262351362 |
ISBN-13 |
: 0262351366 |
Rating |
: 4/5 (62 Downloads) |
Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.