Knowledge Discovery With Support Vector Machines

Knowledge Discovery With Support Vector Machines Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Knowledge Discovery With Support Vector Machines book. This book definitely worth reading, it is an incredibly well-written.

Knowledge Discovery with Support Vector Machines

Lutz H. Hamel
Knowledge Discovery with Support Vector Machines Book PDF

Author : Lutz H. Hamel
Publisher : John Wiley & Sons
Page : 211 pages
File Size : 53,7 Mb
Release : 2011-09-20
Category : Computers
ISBN : 9781118211038

Get Book

Knowledge Discovery with Support Vector Machines by Lutz H. Hamel Pdf

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.

Support Vector Machines

Naiyang Deng,Yingjie Tian,Chunhua Zhang
Support Vector Machines Book PDF

Author : Naiyang Deng,Yingjie Tian,Chunhua Zhang
Publisher : CRC Press
Page : 345 pages
File Size : 53,6 Mb
Release : 2012-12-17
Category : Business & Economics
ISBN : 9781439857939

Get Book

Support Vector Machines by Naiyang Deng,Yingjie Tian,Chunhua Zhang Pdf

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

Data Analysis, Machine Learning and Knowledge Discovery

Myra Spiliopoulou,Lars Schmidt-Thieme,Ruth Janning
Data Analysis Machine Learning and Knowledge Discovery Book PDF

Author : Myra Spiliopoulou,Lars Schmidt-Thieme,Ruth Janning
Publisher : Springer Science & Business Media
Page : 470 pages
File Size : 51,7 Mb
Release : 2013-11-26
Category : Computers
ISBN : 9783319015958

Get Book

Data Analysis, Machine Learning and Knowledge Discovery by Myra Spiliopoulou,Lars Schmidt-Thieme,Ruth Janning Pdf

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012. ​

Machine Learning for Knowledge Discovery with R

Kao-Tai Tsai
Machine Learning for Knowledge Discovery with R Book PDF

Author : Kao-Tai Tsai
Publisher : CRC Press
Page : 267 pages
File Size : 52,9 Mb
Release : 2021-09-15
Category : Business & Economics
ISBN : 9781000450354

Get Book

Machine Learning for Knowledge Discovery with R by Kao-Tai Tsai Pdf

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction of data analysis. It includes many recent supervised and unsupervised machine learning methodologies such as recursive partitioning modelling, regularized regression, support vector machine, neural network, clustering, and causal-effect inference. Additionally, it emphasizes statistical thinking of data analysis, use of statistical graphs for data structure exploration, and result presentations. The book includes many real-world data examples from life-science, finance, etc. to illustrate the applications of the methods described therein. Key Features: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking, judgment, graphical methods, and collaboration with subject-matter-experts in analysis, interpretation, and presentations. Written by statistical data analysis practitioner for practitioners. The book is suitable for upper-level-undergraduate or graduate-level data analysis course. It also serves as a useful desk-reference for data analysts in scientific research or industrial applications.

Content-Addressable Memories

Teuvo Kohonen
Content Addressable Memories Book PDF

Author : Teuvo Kohonen
Publisher : Springer Science & Business Media
Page : 397 pages
File Size : 46,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642830563

Get Book

Content-Addressable Memories by Teuvo Kohonen Pdf

Due to continual progress in the large-scale integration of semiconductor circuits, parallel computing principles can already be met in low-cost sys tems: numerous examples exist in image processing, for which special hard ware is implementable with quite modest resources even by nonprofessional designers. Principles of content addressing, if thoroughly understood, can thereby be applied effectively using standard components. On the other hand, mass storage based on associative principles still exists only in the long term plans of computer technologists. This situation is somewhat confused by the fact that certain expectations are held for the development of new storage media such as optical memories and "spin glasses" (metal alloys with low-density magnetic impurities). Their technologies, however, may not ripen until after "fifth generation" computers have been built. It seems that software methods for content addressing, especially those based on hash coding principles, are still holding their position firmly, and a few innovations have been developed recently. As they need no special hardware, one might expect that they will spread to a wide circle of users. This monograph is based on an extensive literature survey, most of which was published in the First Edition. I have added Chap. ?, which contains a review of more recent work. This updated book now has references to over 1200 original publications. In the editing of the new material, I received valuable help from Anneli HeimbUrger, M. Sc. , and Mrs. Leila Koivisto.

Machine Learning and Knowledge Discovery in Databases

Annalisa Appice,Pedro Pereira Rodrigues,Vítor Santos Costa,João Gama,Alípio Jorge,Carlos Soares
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Annalisa Appice,Pedro Pereira Rodrigues,Vítor Santos Costa,João Gama,Alípio Jorge,Carlos Soares
Publisher : Unknown
Page : 128 pages
File Size : 44,9 Mb
Release : 2015
Category : Electronic
ISBN : 3319235265

Get Book

Machine Learning and Knowledge Discovery in Databases by Annalisa Appice,Pedro Pereira Rodrigues,Vítor Santos Costa,João Gama,Alípio Jorge,Carlos Soares Pdf

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Advanced Methods for Knowledge Discovery from Complex Data

Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook
Advanced Methods for Knowledge Discovery from Complex Data Book PDF

Author : Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook
Publisher : Springer Science & Business Media
Page : 375 pages
File Size : 45,5 Mb
Release : 2006-05-06
Category : Computers
ISBN : 9781846282843

Get Book

Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik,Lawrence B. Holder,Diane J. Cook Pdf

The growth in the amount of data collected and generated has exploded in recent times with the widespread automation of various day-to-day activities, advances in high-level scienti?c and engineering research and the development of e?cient data collection tools. This has given rise to the need for automa- callyanalyzingthedatainordertoextractknowledgefromit,therebymaking the data potentially more useful. Knowledge discovery and data mining (KDD) is the process of identifying valid, novel, potentially useful and ultimately understandable patterns from massive data repositories. It is a multi-disciplinary topic, drawing from s- eral ?elds including expert systems, machine learning, intelligent databases, knowledge acquisition, case-based reasoning, pattern recognition and stat- tics. Many data mining systems have typically evolved around well-organized database systems (e.g., relational databases) containing relevant information. But, more and more, one ?nds relevant information hidden in unstructured text and in other complex forms. Mining in the domains of the world-wide web, bioinformatics, geoscienti?c data, and spatial and temporal applications comprise some illustrative examples in this regard. Discovery of knowledge, or potentially useful patterns, from such complex data often requires the - plication of advanced techniques that are better able to exploit the nature and representation of the data. Such advanced methods include, among o- ers, graph-based and tree-based approaches to relational learning, sequence mining, link-based classi?cation, Bayesian networks, hidden Markov models, neural networks, kernel-based methods, evolutionary algorithms, rough sets and fuzzy logic, and hybrid systems. Many of these methods are developed in the following chapters.

Rule Extraction from Support Vector Machines

Joachim Diederich
Rule Extraction from Support Vector Machines Book PDF

Author : Joachim Diederich
Publisher : Springer
Page : 262 pages
File Size : 50,8 Mb
Release : 2007-12-27
Category : Technology & Engineering
ISBN : 9783540753902

Get Book

Rule Extraction from Support Vector Machines by Joachim Diederich Pdf

Support vector machines (SVMs) are one of the most active research areas in machine learning. SVMs have shown good performance in a number of applications, including text and image classification. However, the learning capability of SVMs comes at a cost – an inherent inability to explain in a comprehensible form, the process by which a learning result was reached. Hence, the situation is similar to neural networks, where the apparent lack of an explanation capability has led to various approaches aiming at extracting symbolic rules from neural networks. For SVMs to gain a wider degree of acceptance in fields such as medical diagnosis and security sensitive areas, it is desirable to offer an explanation capability. User explanation is often a legal requirement, because it is necessary to explain how a decision was reached or why it was made. This book provides an overview of the field and introduces a number of different approaches to extracting rules from support vector machines developed by key researchers. In addition, successful applications are outlined and future research opportunities are discussed. The book is an important reference for researchers and graduate students, and since it provides an introduction to the topic, it will be important in the classroom as well. Because of the significance of both SVMs and user explanation, the book is of relevance to data mining practitioners and data analysts.

Machine Learning and Its Applications

Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos
Machine Learning and Its Applications Book PDF

Author : Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos
Publisher : Springer
Page : 324 pages
File Size : 55,6 Mb
Release : 2003-06-29
Category : Computers
ISBN : 9783540446736

Get Book

Machine Learning and Its Applications by Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos Pdf

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

Learning with Kernels

Bernhard Scholkopf,Alexander J. Smola
Learning with Kernels Book PDF

Author : Bernhard Scholkopf,Alexander J. Smola
Publisher : MIT Press
Page : 645 pages
File Size : 40,6 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9780262536578

Get Book

Learning with Kernels by Bernhard Scholkopf,Alexander J. Smola Pdf

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.

Machine Learning and Knowledge Discovery in Databases

Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Machine Learning and Knowledge Discovery in Databases Book PDF

Author : Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski
Publisher : Springer
Page : 866 pages
File Size : 54,5 Mb
Release : 2017-12-29
Category : Computers
ISBN : 9783319712468

Get Book

Machine Learning and Knowledge Discovery in Databases by Michelangelo Ceci,Jaakko Hollmén,Ljupčo Todorovski,Celine Vens,Sašo Džeroski Pdf

The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track.

Reliable Knowledge Discovery

Honghua Dai,James N. K. Liu,Evgueni Smirnov
Reliable Knowledge Discovery Book PDF

Author : Honghua Dai,James N. K. Liu,Evgueni Smirnov
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 54,8 Mb
Release : 2012-02-23
Category : Computers
ISBN : 9781461419037

Get Book

Reliable Knowledge Discovery by Honghua Dai,James N. K. Liu,Evgueni Smirnov Pdf

Reliable Knowledge Discovery focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military. Reliable Knowledge Discovery also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters. Reliable Knowledge Discovery is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.

Soft Computing for Knowledge Discovery and Data Mining

Oded Maimon,Lior Rokach
Soft Computing for Knowledge Discovery and Data Mining Book PDF

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 431 pages
File Size : 41,8 Mb
Release : 2007-10-25
Category : Computers
ISBN : 9780387699356

Get Book

Soft Computing for Knowledge Discovery and Data Mining by Oded Maimon,Lior Rokach Pdf

Data Mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. This book introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining and includes various real-world case studies with detailed results.

Data Mining and Knowledge Discovery Handbook

Oded Maimon,Lior Rokach
Data Mining and Knowledge Discovery Handbook Book PDF

Author : Oded Maimon,Lior Rokach
Publisher : Springer Science & Business Media
Page : 1378 pages
File Size : 42,6 Mb
Release : 2006-05-28
Category : Computers
ISBN : 9780387254654

Get Book

Data Mining and Knowledge Discovery Handbook by Oded Maimon,Lior Rokach Pdf

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Advances in Knowledge Discovery and Data Mining

Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho
Publisher : Springer Science & Business Media
Page : 1098 pages
File Size : 42,9 Mb
Release : 2009-04-20
Category : Computers
ISBN : 9783642013065

Get Book

Advances in Knowledge Discovery and Data Mining by Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho Pdf

This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.