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Author:
Publisher:
ISBN:
Category : Induction (Logic)
Languages : en
Pages : 540
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Book Description
Author:
Publisher:
ISBN:
Category : Induction (Logic)
Languages : en
Pages : 540
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Book Description
Author: Ramon Lopez De Mantaras
Publisher:
ISBN: 9783662208434
Category :
Languages : en
Pages : 484
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Book Description
Author: Ramon Lopez de Mantaras
Publisher: Springer Science & Business Media
ISBN: 3540676023
Category : Computers
Languages : en
Pages : 469
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Book Description
This book constitutes the refereed proceedings of the 11th European Conference on Machine Learning, ECML 2000, held in Barcelona, Catalonia, Spain, in May/June 2000. The 20 long papers and 23 short papers presented together with 2 invited contributions were carefully reviewed and selected from 100 submissions. All current issues in machine learning as well as advanced applications in various areas are addressed.
Author: Matthieu Cord
Publisher: Springer Science & Business Media
ISBN: 3540751718
Category : Computers
Languages : en
Pages : 297
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Book Description
Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.
Author: Shahar Mendelson
Publisher: Springer Science & Business Media
ISBN: 3540005293
Category : Computers
Languages : en
Pages : 267
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Book Description
This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.
Author: Carlos Gershenson
Publisher: World Scientific
ISBN: 9814476013
Category : Science
Languages : en
Pages : 359
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Book Description
Scientific, technological, and cultural changes have always had an impact upon philosophy. They can force a change in the way we perceive the world, reveal new kinds of phenomena to be understood, and provide new ways of understanding phenomena. Complexity science, immersed in a culture of information, is having a diverse but particularly significant impact upon philosophy. Previous ideas do not necessarily sit comfortably with the new paradigm, resulting in new ideas or new interpretations of old ideas.In this unprecedented interdisciplinary volume, researchers from different backgrounds join efforts to update thinking upon philosophical questions with developments in the scientific study of complex systems. The contributions focus on a wide range of topics, but share the common goal of increasing our understanding and improving our descriptions of our complex world. This revolutionary debate includes contributions from leading experts, as well as young researchers proposing fresh ideas.
Author: Katharina Morik
Publisher: Springer Science & Business Media
ISBN: 3540265430
Category : Computers
Languages : en
Pages : 242
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Book Description
This collection of 13 selected papers originates from the International Seminar on Local Pattern Detection, held in Dagstuhl Castle, Germany in April 2004. This state-of-the-art survey on the emerging field Local Pattern Detection addresses four main topics. Three papers cover frequent set mining, four cover subgroup discovery, three cover the statistical view, and three papers are devoted to time phenomena.
Author: Robert Godin
Publisher: Springer
ISBN: 3540322620
Category : Computers
Languages : en
Pages : 428
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Book Description
This volume contains the Proceedings of ICFCA 2005, the 3rd International Conference on Formal Concept Analysis. The ICFCA conference series aims to be the premier forum for the publication of advances in applied lattice and order theory, and in particular scienti?c advances related to formal concept analysis. Formal concept analysis is a ?eld of applied mathematics with its mat- matical root in order theory, in particular in the theory of complete lattices. Researchers had long been aware of the fact that these ?elds have many - tential applications. Formal concept analysis emerged in the 1980s from e?orts to restructure lattice theory to promote better communication between lattice theorists and potential users of lattice theory. The key theme was the mathe- tization of concept and conceptual hierarchy. Since then, the ?eld has developed into a growing research area in its own right with a thriving theoretical com- nity and an increasing number of applications in data and knowledge processing, including data visualization, information retrieval, machine learning, data an- ysis and knowledge management. ICFCA2005re?ectedbothpracticalbene?tsandprogressinthefoundational theory of formal concept analysis. Algorithmic aspects were discussed as well as e?orts to broaden the ?eld. All regular papers appearing in this volume were refereed by at least two, in most cases three independent reviewers. The ?nal decision to accept the papers was arbitrated by the Program Chairs based on the referee reports. It was the involvement of the Program Committee and the Editorial Board that ensured the scienti?c quality of these proceedings.
Author: Pavel Brazdil
Publisher: Springer Nature
ISBN: 3030670244
Category : Artificial intelligence
Languages : en
Pages : 349
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Book Description
Intro -- Preface -- Contents -- Part I Basic Concepts and Architecture -- 1 Introduction -- 1.1 Organization of the Book -- 1.2 Basic Concepts and Architecture (Part I) -- 1.3 Advanced Techniques and Methods (Part II) -- 1.4 Repositories of Experimental Results (Part III) -- References -- 2 Metalearning Approaches for Algorithm Selection I (Exploiting Rankings) -- 2.1 Introduction -- 2.2 Different Forms of Recommendation -- 2.3 Ranking Models for Algorithm Selection -- 2.4 Using a Combined Measure of Accuracy and Runtime -- 2.5 Extensions and Other Approaches -- References -- 3 Evaluating Recommendations of Metalearning/AutoML Systems -- 3.1 Introduction -- 3.2 Methodology for Evaluating Base-Level Algorithms -- 3.3 Normalization of Performance for Base-Level Algorithms -- 3.4 Methodology for Evaluating Metalearning and AutoML Systems -- 3.5 Evaluating Recommendations by Correlation -- 3.6 Evaluating the Effects of Recommendations -- 3.7 Some Useful Measures -- References -- 4 Dataset Characteristics (Metafeatures) -- 4.1 Introduction -- 4.2 Data Characterization Used in Classification Tasks -- 4.3 Data Characterization Used in Regression Tasks -- 4.4 Data Characterization Used in Time Series Tasks -- 4.5 Data Characterization Used in Clustering Tasks -- 4.6 Deriving New Features from the Basic Set -- 4.7 Selection of Metafeatures -- 4.8 Algorithm-Specific Characterization and Representation Issues -- 4.9 Establishing Similarity Between Datasets -- References -- 5 Metalearning Approaches for Algorithm Selection II -- 5.1 Introduction -- 5.2 Using Regression Models in Metalearning Systems -- 5.3 Using Classification at Meta-level for the Prediction of Applicability -- 5.4 Methods Based on Pairwise Comparisons -- 5.5 Pairwise Approach for a Set of Algorithms -- 5.6 Iterative Approach of Conducting Pairwise Tests -- 5.7 Using ART Trees and Forests.
Author: Xiaofeng Chen
Publisher: Springer Nature
ISBN: 3030306194
Category : Computers
Languages : en
Pages : 411
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Book Description
This book constitutes the proceedings of the Second International Conference on Machine Learning for Cyber Security, ML4CS 2019, held in Xi’an, China in September 2019. The 23 revised full papers and 3 short papers presented were carefully reviewed and selected from 70 submissions. The papers detail all aspects of machine learning in network infrastructure security, in network security detections and in application software security.
