Advances In Kernel Methods PDF Download
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Author | : Bernhard Schölkopf |
Publisher | : MIT Press |
Total Pages | : 400 |
Release | : 1999 |
Genre | : Computers |
ISBN | : 9780262194167 |
Download Advances in Kernel Methods Book in PDF, ePub and Kindle
A young girl hears the story of her great-great-great-great- grandfather and his brother who came to the United States to make a better life for themselves helping to build the transcontinental railroad.
Author | : Bernhard SchoÌ?lkopf |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : Algorithms |
ISBN | : 9780262283199 |
Download Advances in Kernel Methods Book in PDF, ePub and Kindle
Author | : Bernhard Scholkopf |
Publisher | : MIT Press |
Total Pages | : 645 |
Release | : 2018-06-05 |
Genre | : Computers |
ISBN | : 0262536579 |
Download Learning with Kernels Book in PDF, ePub and Kindle
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 | : Bernhard Schölkopf |
Publisher | : MIT Press |
Total Pages | : 428 |
Release | : 2004 |
Genre | : Computers |
ISBN | : 9780262195096 |
Download Kernel Methods in Computational Biology Book in PDF, ePub and Kindle
A detailed overview of current research in kernel methods and their application to computational biology.
Author | : Gustau Camps-Valls |
Publisher | : John Wiley & Sons |
Total Pages | : 434 |
Release | : 2009-09-03 |
Genre | : Technology & Engineering |
ISBN | : 0470749008 |
Download Kernel Methods for Remote Sensing Data Analysis Book in PDF, ePub and Kindle
Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.
Author | : S. Y. Kung |
Publisher | : Cambridge University Press |
Total Pages | : 617 |
Release | : 2014-04-17 |
Genre | : Computers |
ISBN | : 1139867636 |
Download Kernel Methods and Machine Learning Book in PDF, ePub and Kindle
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 | : Jose Luis Rojo-Alvarez |
Publisher | : John Wiley & Sons |
Total Pages | : 665 |
Release | : 2018-02-05 |
Genre | : Technology & Engineering |
ISBN | : 1118611799 |
Download Digital Signal Processing with Kernel Methods Book in PDF, ePub and Kindle
A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors: http://github.com/DSPKM • Presents the necessary basic ideas from both digital signal processing and machine learning concepts • Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing • Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.
Author | : Bernhard Schölkopf |
Publisher | : MIT Press |
Total Pages | : 658 |
Release | : 2002 |
Genre | : Computers |
ISBN | : 9780262194754 |
Download Learning with Kernels Book in PDF, ePub and Kindle
A comprehensive introduction to Support Vector Machines and related kernel methods.
Author | : John Shawe-Taylor |
Publisher | : Cambridge University Press |
Total Pages | : 520 |
Release | : 2004-06-28 |
Genre | : Computers |
ISBN | : 9780521813976 |
Download Kernel Methods for Pattern Analysis Book in PDF, ePub and Kindle
Publisher Description
Author | : Francesco Camastra |
Publisher | : Springer |
Total Pages | : 564 |
Release | : 2015-07-21 |
Genre | : Computers |
ISBN | : 144716735X |
Download Machine Learning for Audio, Image and Video Analysis Book in PDF, ePub and Kindle
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.