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  • 1
    UID:
    b3kat_BV013203038
    Format: XII, 404 S. , graph. Darst.
    ISBN: 3540677046
    Series Statement: Lecture notes in computer science 1857
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Automatische Klassifikation ; Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Klassifikator ; Dokumentanalyse ; Klassifikator ; Klassifikator ; Neuronales Netz ; Konferenzschrift ; Konferenzschrift ; Kongress ; Konferenzschrift ; Konferenzschrift
    URL: Cover
    Author information: Kittler, Josef 1946-
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  • 2
    UID:
    b3kat_BV013779335
    Format: XII, 456 S. , graph. Darst.
    ISBN: 3540422846
    Series Statement: Lecture notes in computer science 2096
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Klassifikator ; Dokumentanalyse ; Klassifikator ; Klassifikator ; Neuronales Netz ; Konferenzschrift ; Kongress ; Konferenzschrift ; Konferenzschrift
    URL: Cover
    Author information: Kittler, Josef 1946-
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  • 3
    UID:
    gbv_314722750
    Format: XII, 404 S. , graph. Darst.
    ISBN: 3540677046
    Series Statement: Lecture notes in computer science 1857
    Note: Literaturangaben
    Additional Edition: Erscheint auch als Online-Ausgabe Goos, Gerhard Multiple Classifier Systems Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg, 2000 ISBN 9783540450146
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Dokumentanalyse ; Neuronales Netz ; Konferenzschrift
    URL: Cover
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  • 4
    Online Resource
    Online Resource
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    UID:
    gbv_164644843X
    Format: Online-Ressource , v.: digital
    ISBN: 9783540482192
    Series Statement: Lecture Notes in Computer Science 2096
    Content: Bagging and Boosting -- Bagging and the Random Subspace Method for Redundant Feature Spaces -- Performance Degradation in Boosting -- A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models -- Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis -- Learning Classification RBF Networks by Boosting -- MCS Design Methodology -- Data Complexity Analysis for Classifier Combination -- Genetic Programming for Improved Receiver Operating Characteristics -- Methods for Designing Multiple Classifier Systems -- Decision-Level Fusion in Fingerprint Verification -- Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition -- Combined Classification of Handwritten Digits Using the ‘Virtual Test Sample Method’ -- Averaging Weak Classifiers -- Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds -- Ensemble Classifiers -- Multiple Classifier Systems Based on Interpretable Linear Classifiers -- Least Squares and Estimation Measures via Error Correcting Output Code -- Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis -- Information Analysis of Multiple Classifier Fusion? -- Limiting the Number of Trees in Random Forests -- Learning-Data Selection Mechanism through Neural Networks Ensemble -- A Multi-SVM Classification System -- Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System -- Feature Spaces for MCS -- Feature Weighted Ensemble Classifiers – A Modified Decision Scheme -- Feature Subsets for Classifier Combination: An Enumerative Experiment -- Input Decimation Ensembles: Decorrelation through Dimensionality Reduction -- Classifier Combination as a Tomographic Process -- MCS in Remote Sensing -- A Robust Multiple Classifier System for a Partially Unsupervised Updating of Land-Cover Maps -- Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances -- Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data -- Solar Wind Data Analysis Using Self-Organizing Hierarchical Neural Network Classifiers -- One Class MCS and Clustering -- Combining One-Class Classifiers -- Finding Consistent Clusters in Data Partitions -- A Self-Organising Approach to Multiple Classifier Fusion -- Combination Strategies -- Error Rejection in Linearly Combined Multiple Classifiers -- Relationship of Sum and Vote Fusion Strategies -- Complexity of Data Subsets Generated by the Random Subspace Method: An Experimental Investigation -- On Combining Dissimilarity Representations -- Application of Multiple Classifier Techniques to Subband Speaker Identification with an HMM/ANN System -- Classification of Time Series Utilizing Temporal and Decision Fusion -- Use of Positional Information in Sequence Alignment for Multiple Classifier Combination -- Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting -- Tree-Structured Support Vector Machines for Multi-class Pattern Recognition -- On the Combination of Different Template Matching Strategies for Fast Face Detection -- Improving Product by Moderating k-NN Classifiers -- Automatic Model Selection in a Hybrid Perceptron/Radial Network.
    Content: Driven by the requirements of a large number of practical and commercially - portant applications, the last decade has witnessed considerable advances in p- tern recognition. Better understanding of the design issues and new paradigms, such as the Support Vector Machine, have contributed to the development of - proved methods of pattern classi cation. However, while any performance gains are welcome, and often extremely signi cant from the practical point of view, it is increasingly more challenging to reach the point of perfection as de ned by the theoretical optimality of decision making in a given decision framework. The asymptoticity of gains that can be made for a single classi er is a re?- tion of the fact that any particular design, regardless of how good it is, simply provides just one estimate of the optimal decision rule. This observation has motivated the recent interest in Multiple Classi er Systems , which aim to make use of several designs jointly to obtain a better estimate of the optimal decision boundary and thus improve the system performance. This volume contains the proceedings of the international workshop on Multiple Classi er Systems held at Robinson College, Cambridge, United Kingdom (July 2{4, 2001), which was organized to provide a forum for researchers in this subject area to exchange views and report their latest results.
    Note: In: Springer-Online
    Additional Edition: ISBN 9783540422846
    Additional Edition: Buchausg. u.d.T. Multiple classifier systems Berlin [u.a.] : Springer, 2001 ISBN 3540422846
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Klassifikator ; Dokumentanalyse ; Klassifikator ; Klassifikator ; Neuronales Netz ; Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Dokumentanalyse ; Neuronales Netz ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 5
    UID:
    gbv_336269331
    Format: Online-Ressource, xii, 456 p., text and images
    Edition: [Elektronische Ressource]
    Edition: 2001 Springer eBook collection. Computer science
    ISBN: 3540482199 , 9783540482192
    Series Statement: Lecture notes in computer science 2096
    Additional Edition: ISBN 3540422846
    Additional Edition: ISBN 9783540422846
    Language: English
    Keywords: Maschinelles Lernen ; Klassifikator ; Fernerkundung ; Bildanalyse ; Klassifikator ; Dokumentanalyse ; Klassifikator ; Klassifikator ; Neuronales Netz ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
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  • 6
    UID:
    gbv_1647675839
    Format: Online-Ressource (digital)
    ISBN: 9783540926955
    Series Statement: Lecture Notes in Computer Science 5313
    Content: Nested Partitioning for the Minimum Energy Broadcast Problem -- An Adaptive Memory-Based Approach Based on Partial Enumeration -- Learning While Optimizing an Unknown Fitness Surface -- On Effectively Finding Maximal Quasi-cliques in Graphs -- Improving the Exploration Strategy in Bandit Algorithms -- Learning from the Past to Dynamically Improve Search: A Case Study on the MOSP Problem -- Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm -- Explicit and Emergent Cooperation Schemes for Search Algorithms -- Multiobjective Landscape Analysis and the Generalized Assignment Problem -- Limited-Memory Techniques for Sensor Placement in Water Distribution Networks -- A Hybrid Clustering Algorithm Based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure -- Ant Colony Optimization and the Minimum Spanning Tree Problem -- A Vector Assignment Approach for the Graph Coloring Problem -- Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications -- Tuning Local Search by Average-Reward Reinforcement Learning -- Evolution of Fitness Functions to Improve Heuristic Performance -- A Continuous Characterization of Maximal Cliques in k-Uniform Hypergraphs -- Hybrid Heuristics for Multi-mode Resource-Constrained Project Scheduling.
    Content: This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Learning and Intelligent Optimization, LION 2007 II, held in Trento, Italy, in December 2007. The 18 revised full papers were carefully reviewed and selected from 48 submissions for inclusion in the book. The papers cover current issues of machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems and are organized in topical sections on improving optimization through learning, variable neighborhood search, insect colony optimization, applications, new paradigms, cliques, stochastic optimization, combinatorial optimization, fitness and landscapes, and particle swarm optimization.
    Additional Edition: ISBN 9783540926948
    Additional Edition: Buchausg. u.d.T. Learning and intelligent optimization Berlin : Springer, 2008 ISBN 3540926941
    Additional Edition: ISBN 9783540926948
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen ; NP-hartes Problem ; Soft Computing ; Metaheuristik ; N-armiger Bandit ; Ameisenalgorithmus ; Lernendes System ; Organic Computing ; Optimierung ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    Author information: Pandu Rangan, C. 1955-
    Author information: Mattern, Friedemann 1955-
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  • 7
    UID:
    gbv_1652326502
    Format: Online-Ressource (XI, 400 p. 106 illus, online resource)
    ISBN: 9783642380679
    Series Statement: Lecture Notes in Computer Science 7872
    Content: This book constitutes the refereed proceedings of the 11th International Workshop on Multiple Classifier Systems, MCS 2013, held in Nanjing, China, in May 2013. The 34 revised papers presented together with two invited papers were carefully reviewed and selected from 59 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics
    Note: Literaturangaben
    Additional Edition: ISBN 9783642380662
    Additional Edition: Erscheint auch als Druck-Ausgabe Multiple classifier systems Berlin : Springer, 2013 ISBN 3642380662
    Additional Edition: ISBN 9783642380662
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Automatische Klassifikation ; Mustererkennung ; Maschinelles Lernen ; Neuronales Netz ; Data Mining ; Statistisches Modell ; Konferenzschrift ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 8
    UID:
    gbv_669345415
    Format: Online-Ressource (XII, 372 S.)
    Edition: Online-Ausg. 2011 Springer eBook collection. Computer science Electronic reproduction; Available via World Wide Web
    ISBN: 9783642215575
    Series Statement: Lecture notes in computer science 6713
    Note: Literaturangaben , Electronic reproduction; Available via World Wide Web
    Additional Edition: ISBN 3642215564
    Additional Edition: ISBN 9783642215568
    Additional Edition: Erscheint auch als Druck-Ausgabe Multiple classifier systems Berlin : Springer, 2011 ISBN 3642215564
    Additional Edition: ISBN 9783642215568
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Automatische Klassifikation ; Mustererkennung ; Maschinelles Lernen ; Datensicherung ; Statistisches Modell ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
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  • 9
    UID:
    gbv_1657319296
    Format: Online-Ressource (X, 231 p. 40 illus, online resource)
    ISBN: 9783319202488
    Series Statement: Lecture Notes in Computer Science 9132
    Content: A Novel Bagging Ensemble Approach for Variable Ranking and Selection for Linear Regression Models -- A Hierarchical Ensemble Method for DAG-Structured Taxonomies -- Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble -- Fractional Programming Weighted Decoding for Error-Correcting Output Codes -- Instance-Based Decompositions of Error Correcting Output Codes -- Pruning Bagging Ensembles with Metalearning -- Multi-label Selective Ensemble -- Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation -- Detecting Ordinal Class Structures -- Calibrating AdaBoost for Asymmetric Learning -- Building Classifier Ensembles Using Greedy Graph Edit Distance -- Measuring the Stability of Feature Selection with Applications to Ensemble Methods -- Suboptimal Graph Edit Distance Based on Sorted Local Assignments -- Multimodal PLSA for Movie Genre Classification -- One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time -- An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees -- Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective -- An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles -- Bio-Visual Fusion for Person Independent Recognition of Pain Intensity.
    Content: This book constitutes the refereed proceedings of the 12th International Workshop on Multiple Classifier Systems, MCS 2015, held in Günzburg, Germany, in June/July 2015. The 19 revised papers presented were carefully reviewed and selected from 25 submissions. The papers address issues in multiple classifier systems and ensemble methods, including pattern recognition, machine learning, neural network, data mining and statistics. They are organized in topical sections on theory and algorithms and application and evaluation.
    Note: Literaturangaben , A Novel Bagging Ensemble Approach for Variable Ranking and Selection for Linear Regression ModelsA Hierarchical Ensemble Method for DAG-Structured Taxonomies -- Diversity Measures and Margin Criteria in Multi-class Majority Vote Ensemble -- Fractional Programming Weighted Decoding for Error-Correcting Output Codes -- Instance-Based Decompositions of Error Correcting Output Codes -- Pruning Bagging Ensembles with Metalearning -- Multi-label Selective Ensemble -- Supervised Selective Combination of Diverse Object-Representation Modalities for Regression Estimation -- Detecting Ordinal Class Structures -- Calibrating AdaBoost for Asymmetric Learning -- Building Classifier Ensembles Using Greedy Graph Edit Distance -- Measuring the Stability of Feature Selection with Applications to Ensemble Methods -- Suboptimal Graph Edit Distance Based on Sorted Local Assignments -- Multimodal PLSA for Movie Genre Classification -- One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time -- An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees -- Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective -- An Empirical Investigation on the Use of Diversity for Creation of Classifier Ensembles -- Bio-Visual Fusion for Person Independent Recognition of Pain Intensity.
    Additional Edition: ISBN 9783319202471
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-331-92024-7-1
    Additional Edition: Erscheint auch als Druck-Ausgabe Multiple classifier systems Cham [u.a.] : Springer, 2015 ISBN 9783319202471
    Language: English
    Keywords: Automatische Klassifikation ; Mustererkennung ; Maschinelles Lernen ; Datensicherung ; Statistisches Modell ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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  • 10
    Online Resource
    Online Resource
    Berlin, Heidelberg : Springer-Verlag Berlin Heidelberg
    UID:
    gbv_1649268661
    Format: Online-Ressource
    ISBN: 9783540454281 , 3540438181
    Series Statement: Lecture Notes in Computer Science 2364
    Content: This book constitutes the refereed proceedings of the Third International Workshop on Multiple Classifier Systems, MCS 2002, held in Cagliari, Italy, in June 2002.The 29 revised full papers presented together with three invited papers were carefully reviewed and selected for inclusion in the volume. The papers are organized in topical sections on bagging and boosting, ensemble learning and neural networks, design methodologies, combination strategies, analysis and performance evaluation, and applications
    Additional Edition: ISBN 9783540438182
    Additional Edition: Buchausg. u.d.T. Multiple classifier systems Berlin : Springer, 2002 ISBN 3540438181
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Maschinelles Lernen ; Klassifikator ; Klassifikator ; Neuronales Netz ; Data Mining ; Mustererkennung ; Automatische Klassifikation ; Konferenzschrift
    URL: Volltext  (lizenzpflichtig)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
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