Learning With Fractional Orthogonal Kernel Classifiers In Support Vector Machines
DOWNLOAD
Download Learning With Fractional Orthogonal Kernel Classifiers In Support Vector Machines PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Learning With Fractional Orthogonal Kernel Classifiers In Support Vector Machines book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Learning With Fractional Orthogonal Kernel Classifiers In Support Vector Machines
DOWNLOAD
Author : Jamal Amani Rad
language : en
Publisher: Springer Nature
Release Date : 2023-03-18
Learning With Fractional Orthogonal Kernel Classifiers In Support Vector Machines written by Jamal Amani Rad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-18 with Mathematics categories.
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
Dimensionality Reduction In Machine Learning
DOWNLOAD
Author : Jamal Amani Rad
language : en
Publisher: Morgan Kaufmann
Release Date : 2025-02-04
Dimensionality Reduction In Machine Learning written by Jamal Amani Rad and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data. - Provides readers with a comprehensive overview of various dimension reduction algorithms, including linear methods, non-linear methods, and deep learning methods - Covers the implementation aspects of algorithms supported by numerous code examples - Compares different algorithms so the reader can understand which algorithm is suitable for their purpose - Includes algorithm examples that are supported by a Github repository which consists of full notebooks for the programming code
Recent Developments In Fractional Calculus Theory Applications And Numerical Simulations
DOWNLOAD
Author : Lakhveer Kaur
language : en
Publisher: Springer Nature
Release Date : 2025-05-30
Recent Developments In Fractional Calculus Theory Applications And Numerical Simulations written by Lakhveer Kaur and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Technology & Engineering categories.
This book discusses recent developments in fractional calculus and fractional differential equations in a very elaborative manner and is of interest to research scholars, academicians and scientists who want to enhance the knowledge in the context of new insights and mathematical ideas in fractional calculus and its emerging applications in various fields. It focuses on strengthening the existing results along with identifying the practical challenges encountered. The purpose of this collection is to provide comprehension of articles that reflect recent mathematical results as well as some results in applied sciences untouched by the tools and techniques of fractional calculus along with their modelling and computation having applications in diverse arenas.
Proceedings Of The International Conference On Ismac In Computational Vision And Bio Engineering 2018 Ismac Cvb
DOWNLOAD
Author : Durai Pandian
language : en
Publisher: Springer
Release Date : 2019-01-01
Proceedings Of The International Conference On Ismac In Computational Vision And Bio Engineering 2018 Ismac Cvb written by Durai Pandian and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-01 with Technology & Engineering categories.
These are the proceedings of the International Conference on ISMAC-CVB, held in Palladam, India, in May 2018. The book focuses on research to design new analysis paradigms and computational solutions for quantification of information provided by object recognition, scene understanding of computer vision and different algorithms like convolutional neural networks to allow computers to recognize and detect objects in images with unprecedented accuracy and to even understand the relationships between them. The proceedings treat the convergence of ISMAC in Computational Vision and Bioengineering technology and includes ideas and techniques like 3D sensing, human visual perception, scene understanding, human motion detection and analysis, visualization and graphical data presentation and a very wide range of sensor modalities in terms of surveillance, wearable applications, home automation etc. ISMAC-CVB is a forum for leading academic scientists, researchers and research scholars to exchange and share their experiences and research results about all aspects of computational vision and bioengineering.
Dissertation Abstracts International
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2005
Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Dissertations, Academic categories.
Twin Support Vector Machines
DOWNLOAD
Author : Jayadeva
language : en
Publisher: Springer
Release Date : 2016-10-12
Twin Support Vector Machines written by Jayadeva and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-12 with Technology & Engineering categories.
This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
Semi Supervised Learning
DOWNLOAD
Author : Olivier Chapelle
language : en
Publisher: MIT Press
Release Date : 2010-01-22
Semi Supervised Learning written by Olivier Chapelle and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-01-22 with Computers categories.
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.
A Matrix Algebra Approach To Artificial Intelligence
DOWNLOAD
Author : Xian-Da Zhang
language : en
Publisher: Springer Nature
Release Date : 2020-05-23
A Matrix Algebra Approach To Artificial Intelligence written by Xian-Da Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-23 with Computers categories.
Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective. The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.
Introduction To Semi Supervised Learning
DOWNLOAD
Author : Xiaojin Zhu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Introduction To Semi Supervised Learning written by Xiaojin Zhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook
Support Vector Machines
DOWNLOAD
Author : Naiyang Deng
language : en
Publisher: CRC Press
Release Date : 2012-12-17
Support Vector Machines written by Naiyang Deng and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-17 with Business & Economics categories.
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)—classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built. The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twin SVMs for binary classification problems, SVMs for solving multi-classification problems based on ordinal regression, SVMs for semi-supervised problems, and SVMs for problems with perturbations. To improve readability, concepts, methods, and results are introduced graphically and with clear explanations. For important concepts and algorithms, such as the Crammer-Singer SVM for multi-class classification problems, the text provides geometric interpretations that are not depicted in current literature. Enabling a sound understanding of SVMs, this book gives beginners as well as more experienced researchers and engineers the tools to solve real-world problems using SVMs.