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  • 1
    Online Resource
    Online Resource
    Multiple classifier systems : 9th international workshop, MCS 2010, Cairo, Egypt, April 7 - 9, 2010 ; proceedings (2010)
    Berlin [u.a.] : Springer
    Details
    UID:
    b3kat_BV036439934
    Format: 1 Online-Ressource (X, 328 S.) , Ill., graph. Darst.
    ISBN: 9783642121265 , 9783642121272
    Series Statement: Lecture notes in computer science 5997
    Note: Literaturangaben
    Language: English
    Subjects: Computer Science
    RVK:
    SS 4800
    Keywords: Mustererkennung ; Automatische Klassifikation ; Biometrie ; Authentifikation ; Mathematisches Modell ; Künstliche Intelligenz ; Konferenzschrift ; Konferenzschrift
    DOI: 10.1007/978-3-642-12127-2
    URL: Volltext
    URL: Volltext
    URL: Volltext  (lizenzpflichtig)
    URL: Inhaltstext
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    Online Resource
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    UB Potsdam
    BTU Cottbus
  • 2
    Online Resource
    Online Resource
    Multiple Classifier Systems : 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings (2010)
    Berlin, Heidelberg :Springer Berlin Heidelberg,
    Details
    UID:
    almahu_9947364256602882
    Format: X, 328 p. 77 illus. , online resource.
    ISBN: 9783642121272
    Series Statement: Lecture Notes in Computer Science, 5997
    Note: Classifier Ensembles(I) -- Weighted Bagging for Graph Based One-Class Classifiers -- Improving Multilabel Classification Performance by Using Ensemble of Multi-label Classifiers -- New Feature Splitting Criteria for Co-training Using Genetic Algorithm Optimization -- Incremental Learning of New Classes in Unbalanced Datasets: Learn?+?+?.UDNC -- Tomographic Considerations in Ensemble Bias/Variance Decomposition -- Choosing Parameters for Random Subspace Ensembles for fMRI Classification -- Classifier Ensembles(II) -- An Experimental Study on Ensembles of Functional Trees -- Multiple Classifier Systems under Attack -- SOCIAL: Self-Organizing ClassIfier ensemble for Adversarial Learning -- Unsupervised Change-Detection in Retinal Images by a Multiple-Classifier Approach -- A Double Pruning Algorithm for Classification Ensembles -- Estimation of the Number of Clusters Using Multiple Clustering Validity Indices -- Classifier Diversity -- “Good” and “Bad” Diversity in Majority Vote Ensembles -- Multi-information Ensemble Diversity -- Classifier Selection -- Dynamic Selection of Ensembles of Classifiers Using Contextual Information -- Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems -- Combining Multiple Kernels -- A Support Kernel Machine for Supervised Selective Combining of Diverse Pattern-Recognition Modalities -- Combining Multiple Kernels by Augmenting the Kernel Matrix -- Boosting and Bootstrapping -- Class-Separability Weighting and Bootstrapping in Error Correcting Output Code Ensembles -- Boosted Geometry-Based Ensembles -- Online Non-stationary Boosting -- Handwriting Recognition -- Combining Neural Networks to Improve Performance of Handwritten Keyword Spotting -- Combining Committee-Based Semi-supervised and Active Learning and Its Application to Handwritten Digits Recognition -- Using Diversity in Classifier Set Selection for Arabic Handwritten Recognition -- Applications -- Forecast Combination Strategies for Handling Structural Breaks for Time Series Forecasting -- A Multiple Classifier System for Classification of LIDAR Remote Sensing Data Using Multi-class SVM -- A Multi-Classifier System for Off-Line Signature Verification Based on Dissimilarity Representation -- A Multi-objective Sequential Ensemble for Cluster Structure Analysis and Visualization and Application to Gene Expression -- Combining 2D and 3D Features to Classify Protein Mutants in HeLa Cells -- An Experimental Comparison of Hierarchical Bayes and True Path Rule Ensembles for Protein Function Prediction -- Recognizing Combinations of Facial Action Units with Different Intensity Using a Mixture of Hidden Markov Models and Neural Network -- Invited Papers -- Some Thoughts at the Interface of Ensemble Methods and Feature Selection -- Multiple Classifier Systems for the Recogonition of Human Emotions -- Erratum -- Erratum.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9783642121265
    Language: English
    DOI: 10.1007/978-3-642-12127-2
    URL: http://dx.doi.org/10.1007/978-3-642-12127-2
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Open Access Request
    HU Berlin
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