Support Vector Machines And Perceptrons
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Support Vector Machines And Perceptrons
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Author : M.N. Murty
language : en
Publisher: Springer
Release Date : 2016-08-16
Support Vector Machines And Perceptrons written by M.N. Murty and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-16 with Computers categories.
This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>
Learning And Soft Computing
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Author : Vojislav Kecman
language : en
Publisher: MIT Press
Release Date : 2001
Learning And Soft Computing written by Vojislav Kecman and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Knowledge Discovery With Support Vector Machines
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Author : Lutz H. Hamel
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-20
Knowledge Discovery With Support Vector Machines written by Lutz H. Hamel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-20 with Computers categories.
An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.
Efficient Learning Machines
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Author : Mariette Awad
language : en
Publisher: Apress
Release Date : 2015-04-27
Efficient Learning Machines written by Mariette Awad and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-27 with Computers categories.
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
Nonlinear Modeling
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Author : Johan A. K. Suykens
language : en
Publisher: Springer Science & Business Media
Release Date : 1998-06-30
Nonlinear Modeling written by Johan A. K. Suykens and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-06-30 with Language Arts & Disciplines categories.
This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.
Support Vector Machines Theory And Applications
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Author : Lipo Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-06-21
Support Vector Machines Theory And Applications written by Lipo Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-21 with Computers categories.
The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.
Perceptrons
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-25
Perceptrons written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-25 with Computers categories.
What Is Perceptrons The perceptron is a technique for supervised learning of binary classifiers that is used in the field of machine learning. A function known as a binary classifier is one that can determine whether or not an input, which is often portrayed by a vector of numbers, is a member of a particular category. It is a kind of linear classifier, which means that it is a classification method that forms its predictions on the basis of a linear predictor function by combining a set of weights with the feature vector. In other words, it creates its predictions based on a linear predictor function. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Perceptron Chapter 2: Supervised learning Chapter 3: Support vector machine Chapter 4: Linear classifier Chapter 5: Pattern recognition Chapter 6: Artificial neuron Chapter 7: Hopfield network Chapter 8: Backpropagation Chapter 9: Feedforward neural network Chapter 10: Multilayer perceptron (II) Answering the public top questions about perceptrons. (III) Real world examples for the usage of perceptrons in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of perceptrons. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Support Vector Machine
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2024-12-29
Support Vector Machine written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-29 with Technology & Engineering categories.
In the everevolving field of robotics, the application of advanced machine learning techniques is pivotal. “Support Vector Machine,” part of the Robotics Science series, explores the role of support vector machines (SVMs) in revolutionizing robotic systems. Written by Fouad Sabry, this book provides a comprehensive overview, from fundamental concepts to advanced techniques, essential for anyone keen on harnessing SVMs for robotics and automation. Chapters Brief Overview: 1: Support vector machine: Introduction to SVMs, highlighting their importance in classification and regression tasks in robotics. 2: Linear classifier: Explains the basics of linear classifiers, foundational for understanding SVM's functionality. 3: Perceptron: Discusses the perceptron algorithm, a precursor to SVMs, useful in binary classification problems. 4: Projection (linear algebra): Focuses on the geometric concept of projection, crucial for understanding SVM's working principle. 5: Linear separability: Explores the concept of linear separability, the basis for using SVM in linearly separable datasets. 6: Kernel method: Introduces the kernel trick, enabling SVMs to operate in higherdimensional spaces for nonlinear classification. 7: Relevance vector machine: Examines relevance vector machines, a variation of SVMs with fewer support vectors for efficient computation. 8: Online machine learning: Looks at how online learning methods can be applied to SVM for realtime adaptation in robotics. 9: Sequential minimal optimization: Covers the optimization method used to train SVMs efficiently, a core concept for robotic applications. 10: Leastsquares support vector machine: Discusses this alternative SVM formulation to handle regression problems in robotic systems. 11: String kernel: Explores the string kernel, which allows SVMs to handle sequential data, such as robot sensor inputs. 12: Hinge loss: Delves into hinge loss, the function used in SVM to ensure maximum margin classification. 13: Ranking SVM: Looks at ranking SVM, particularly useful in robotics for decisionmaking and prioritization tasks. 14: Regularization perspectives on support vector machines: Explores the role of regularization in controlling overfitting, essential for building reliable robotic systems. 15: Bayesian interpretation of kernel regularization: Offers a Bayesian perspective, linking probabilistic modeling to SVM kernel regularization for more accurate robotics models. 16: Polynomial kernel: Discusses the polynomial kernel, allowing SVM to model nonlinear decision boundaries in robotic tasks. 17: Radial basis function kernel: Covers the radial basis function kernel, a powerful tool for handling complex data patterns in robotic systems. 18: Kernel perceptron: Examines the kernel perceptron method, expanding on SVMs for more advanced robotic tasks. 19: Platt scaling: Introduces Platt scaling, a technique used to convert SVM outputs into probabilistic predictions in robotics. 20: Manifold regularization: Focuses on manifold regularization, helping to generalize SVM models to highdimensional data, often encountered in robotics. 21: Weak supervision: Concludes with weak supervision techniques, essential for improving SVM models in situations with limited labeled data. Whether you're a professional working in robotics, an undergraduate or graduate student, or an enthusiast with a keen interest in machine learning techniques, this book is invaluable. With realworld applications throughout, it delivers insights not only on theoretical concepts but also on how they can be directly applied to robotic systems.
Rough Sets Selected Methods And Applications In Management And Engineering
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Author : Georg Peters
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-02-21
Rough Sets Selected Methods And Applications In Management And Engineering written by Georg Peters and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-21 with Computers categories.
Rough Set Theory, introduced by Pawlak in the early 1980s, has become an important part of soft computing within the last 25 years. However, much of the focus has been on the theoretical understanding of Rough Sets, with a survey of Rough Sets and their applications within business and industry much desired. Rough Sets: Selected Methods and Applications in Management and Engineering provides context to Rough Set theory, with each chapter exploring a real-world application of Rough Sets. Rough Sets is relevant to managers striving to improve their businesses, industry researchers looking to improve the efficiency of their solutions, and university researchers wanting to apply Rough Sets to real-world problems.
Deep Learning For Natural Language Processing
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Author : Stephan Raaijmakers
language : en
Publisher: Simon and Schuster
Release Date : 2022-12-06
Deep Learning For Natural Language Processing written by Stephan Raaijmakers and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-06 with Computers categories.
Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning! Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms. Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses. Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You'll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!