Fusion Methods For Unsupervised Learning Ensembles

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Fusion Methods for Unsupervised Learning Ensembles

Bruno Baruque
Fusion Methods for Unsupervised Learning Ensembles Book PDF

Author : Bruno Baruque
Publisher : Springer
Page : 141 pages
File Size : 54,7 Mb
Release : 2011-03-23
Category : Computers
ISBN : 3642162061

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Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque Pdf

The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.

Fusion Methods for Unsupervised Learning Ensembles

Bruno Baruque
Fusion Methods for Unsupervised Learning Ensembles Book PDF

Author : Bruno Baruque
Publisher : Springer Science & Business Media
Page : 153 pages
File Size : 42,7 Mb
Release : 2010-11-23
Category : Computers
ISBN : 9783642162046

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Fusion Methods for Unsupervised Learning Ensembles by Bruno Baruque Pdf

The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.

Applications of Supervised and Unsupervised Ensemble Methods

Oleg Okun
Applications of Supervised and Unsupervised Ensemble Methods Book PDF

Author : Oleg Okun
Publisher : Springer Science & Business Media
Page : 276 pages
File Size : 50,8 Mb
Release : 2009-10-06
Category : Computers
ISBN : 9783642039980

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Applications of Supervised and Unsupervised Ensemble Methods by Oleg Okun Pdf

Expanding upon presentations at last year’s SUEMA (Supervised and Unsupervised Ensemble Methods and Applications) meeting, this volume explores recent developments in the field. Useful examples act as a guide for practitioners in computational intelligence.

Hybrid Classifiers

Michal Wozniak
Hybrid Classifiers Book PDF

Author : Michal Wozniak
Publisher : Springer
Page : 227 pages
File Size : 46,8 Mb
Release : 2013-09-16
Category : Technology & Engineering
ISBN : 9783642409974

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Hybrid Classifiers by Michal Wozniak Pdf

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

Advances in Machine Learning and Data Mining for Astronomy

Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Advances in Machine Learning and Data Mining for Astronomy Book PDF

Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 744 pages
File Size : 44,6 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841747

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Advances in Machine Learning and Data Mining for Astronomy by Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava Pdf

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Artificial Intelligence: Concepts, Methodologies, Tools, and Applications

Management Association, Information Resources
Artificial Intelligence Concepts Methodologies Tools and Applications Book PDF

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 3048 pages
File Size : 51,9 Mb
Release : 2016-12-12
Category : Computers
ISBN : 9781522517603

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Artificial Intelligence: Concepts, Methodologies, Tools, and Applications by Management Association, Information Resources Pdf

Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.

Advances in Self-Organizing Maps and Learning Vector Quantization

Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll
Advances in Self Organizing Maps and Learning Vector Quantization Book PDF

Author : Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll
Publisher : Springer
Page : 370 pages
File Size : 50,9 Mb
Release : 2016-01-07
Category : Technology & Engineering
ISBN : 9783319285184

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Advances in Self-Organizing Maps and Learning Vector Quantization by Erzsébet Merényi,Michael J. Mendenhall,Patrick O'Driscoll Pdf

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization.The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and Millennium Instituteof Astrophysics, Chile), and Professor Risto Miikkulainen (University of Texas at Austin, USA). The book comprises a diverse set of theoretical works on Self-Organizing Maps, Neural Gas, Learning Vector Quantization and related topics, and an excellent variety of applications to data visualization, clustering, classification, language processing, robotic control, planning, and to the analysis of astronomical data, brain images, clinical data, time series, and agricultural data.

Machine Learning

Yagang Zhang
Machine Learning Book PDF

Author : Yagang Zhang
Publisher : BoD – Books on Demand
Page : 448 pages
File Size : 50,8 Mb
Release : 2010-02-01
Category : Games & Activities
ISBN : 9789533070339

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Machine Learning by Yagang Zhang Pdf

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.

Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques

Kulkarni, Siddhivinayak
Machine Learning Algorithms for Problem Solving in Computational Applications Intelligent Techniques Book PDF

Author : Kulkarni, Siddhivinayak
Publisher : IGI Global
Page : 464 pages
File Size : 47,9 Mb
Release : 2012-06-30
Category : Computers
ISBN : 9781466618343

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Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques by Kulkarni, Siddhivinayak Pdf

Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.

Kernel-based Data Fusion for Machine Learning

Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau
Kernel based Data Fusion for Machine Learning Book PDF

Author : Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau
Publisher : Springer Science & Business Media
Page : 223 pages
File Size : 50,6 Mb
Release : 2011-03-26
Category : Computers
ISBN : 9783642194054

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Kernel-based Data Fusion for Machine Learning by Shi Yu,Léon-Charles Tranchevent,Bart Moor,Yves Moreau Pdf

Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.

Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition)

Lior Rokach
Ensemble Learning Pattern Classification Using Ensemble Methods Second Edition Book PDF

Author : Lior Rokach
Publisher : World Scientific
Page : 301 pages
File Size : 44,9 Mb
Release : 2019-02-27
Category : Computers
ISBN : 9789811201974

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Ensemble Learning: Pattern Classification Using Ensemble Methods (Second Edition) by Lior Rokach Pdf

This updated compendium provides a methodical introduction with a coherent and unified repository of ensemble methods, theories, trends, challenges, and applications. More than a third of this edition comprised of new materials, highlighting descriptions of the classic methods, and extensions and novel approaches that have recently been introduced.Along with algorithmic descriptions of each method, the settings in which each method is applicable and the consequences and tradeoffs incurred by using the method is succinctly featured. R code for implementation of the algorithm is also emphasized.The unique volume provides researchers, students and practitioners in industry with a comprehensive, concise and convenient resource on ensemble learning methods.

Advances in Self-Organizing Maps and Learning Vector Quantization

Thomas Villmann,Frank-Michael Schleif,Marika Kaden,Mandy Lange
Advances in Self Organizing Maps and Learning Vector Quantization Book PDF

Author : Thomas Villmann,Frank-Michael Schleif,Marika Kaden,Mandy Lange
Publisher : Springer
Page : 314 pages
File Size : 43,6 Mb
Release : 2014-06-10
Category : Technology & Engineering
ISBN : 9783319076959

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Advances in Self-Organizing Maps and Learning Vector Quantization by Thomas Villmann,Frank-Michael Schleif,Marika Kaden,Mandy Lange Pdf

The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification. This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks. Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis and time series analysis. Other chapters present the latest theoretical work on self-organizing maps as well as learning vector quantization methods, such as relating those methods to classical statistical decision methods. All the contribution demonstrate that vector quantization methods cover a large range of application areas including data visualization of high-dimensional complex data, advanced decision making and classification or data clustering and data compression.

Ensemble Methods

Zhi-Hua Zhou
Ensemble Methods Book PDF

Author : Zhi-Hua Zhou
Publisher : CRC Press
Page : 234 pages
File Size : 55,9 Mb
Release : 2012-06-06
Category : Business & Economics
ISBN : 9781439830055

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Ensemble Methods by Zhi-Hua Zhou Pdf

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field.After presenting background and terminology, the book cover

Fusion in Computer Vision

Bogdan Ionescu,Jenny Benois-Pineau,Tomas Piatrik,Georges Quénot
Fusion in Computer Vision Book PDF

Author : Bogdan Ionescu,Jenny Benois-Pineau,Tomas Piatrik,Georges Quénot
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 45,7 Mb
Release : 2014-03-25
Category : Computers
ISBN : 9783319056968

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Fusion in Computer Vision by Bogdan Ionescu,Jenny Benois-Pineau,Tomas Piatrik,Georges Quénot Pdf

This book presents a thorough overview of fusion in computer vision, from an interdisciplinary and multi-application viewpoint, describing successful approaches, evaluated in the context of international benchmarks that model realistic use cases. Features: examines late fusion approaches for concept recognition in images and videos; describes the interpretation of visual content by incorporating models of the human visual system with content understanding methods; investigates the fusion of multi-modal features of different semantic levels, as well as results of semantic concept detections, for example-based event recognition in video; proposes rotation-based ensemble classifiers for high-dimensional data, which encourage both individual accuracy and diversity within the ensemble; reviews application-focused strategies of fusion in video surveillance, biomedical information retrieval, and content detection in movies; discusses the modeling of mechanisms of human interpretation of complex visual content.

Biologically-Inspired Techniques for Knowledge Discovery and Data Mining

Alam, Shafiq
Biologically Inspired Techniques for Knowledge Discovery and Data Mining Book PDF

Author : Alam, Shafiq
Publisher : IGI Global
Page : 397 pages
File Size : 44,6 Mb
Release : 2014-05-31
Category : Computers
ISBN : 9781466660793

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Biologically-Inspired Techniques for Knowledge Discovery and Data Mining by Alam, Shafiq Pdf

Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques. Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.