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  • Produktbild: Multiple Classifier Systems

Multiple Classifier Systems 8th International Workshop, MCS 2009, Reykjavik, Iceland, June 10-12, 2009, Proceedings

Aus der Reihe Lecture Notes in Computer Science

48,99 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.06.2009

Herausgeber

Jón Atli Benediktsson + weitere

Verlag

Springer Berlin

Seitenzahl

540

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

832 g

Auflage

2009

Sprache

Englisch

ISBN

978-3-642-02325-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

02.06.2009

Herausgeber

Verlag

Springer Berlin

Seitenzahl

540

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

832 g

Auflage

2009

Sprache

Englisch

ISBN

978-3-642-02325-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Multiple Classifier Systems
  • Produktbild: Multiple Classifier Systems
  • ECOC, Boosting and Bagging.- The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles.- Recoding Error-Correcting Output Codes.- Comparison of Bagging and Boosting Algorithms on Sample and Feature Weighting.- Multi-class Boosting with Class Hierarchies.- MCS in Remote Sensing.- Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis.- Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification.- Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data.- Unbalanced Data and Decision Templates.- Optimal Mean-Precision Classifier.- A Multiple Expert Approach to the Class Imbalance Problem Using Inverse Random under Sampling.- Decision Templates Based RBF Network for Tree-Structured Multiple Classifier Fusion.- Stacked Generalization and Active Learning.- Efficient Online Classification Using an Ensemble of Bayesian Linear Logistic Regressors.- Regularized Linear Models in Stacked Generalization.- Active Grading Ensembles for Learning Visual Quality Control from Multiple Humans.- Multiple Classifier Systems for Adversarial Classification Tasks.- Concept Drift, Missing Values and Random Forest.- Incremental Learning of Variable Rate Concept Drift.- Semi-supervised Co-update of Multiple Matchers.- Handling Multimodal Information Fusion with Missing Observations Using the Neutral Point Substitution Method.- Influence of Hyperparameters on Random Forest Accuracy.- SVM Ensembles.- Ensembles of One Class Support Vector Machines.- Disturbing Neighbors Ensembles for Linear SVM.- Fusion of Graphs, Concepts and Categorical Data.- A Labelled Graph Based Multiple Classifier System.- Cluster Ensembles Based on Vector Space Embeddings of Graphs.- Random Ordinality Ensembles A Novel Ensemble Method for Multi-valued Categorical Data.- True Path Rule Hierarchical Ensembles.- Clustering.- A Study of Semi-supervised Generative Ensembles.- Hierarchical Ensemble Support Cluster Machine.- Multi-scale Stacked Sequential Learning.- Unsupervised Hierarchical Weighted Multi-segmenter.- Ant Clustering Using Ensembles of Partitions.- Classifier and Feature Selection.- Selective Ensemble under Regularization Framework.- Criteria Ensembles in Feature Selection.- Network Protocol Verification by a Classifier Selection Ensemble.- Supervised Selective Combining Pattern Recognition Modalities and Its Application to Signature Verification by Fusing On-Line and Off-Line Kernels.- Theory of MCS.- Improved Uniformity Enforcement in Stochastic Discrimination.- An Information Theoretic Perspective on Multiple Classifier Systems.- Constraints in Weighted Averaging.- FaSS: Ensembles for Stable Learners.- MCS Methods and Applications.- Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data.- Terrain Segmentation with On-Line Mixtures of Experts for Autonomous Robot Navigation.- Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image.- Target Identification from High Resolution Remote Sensing Image by Combining Multiple Classifiers.- Neural Network Optimization for Combinations in Identification Systems.- MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction.- Diversity-Based Classifier Selection for Adaptive Object Tracking.- Ensemble Based Data Fusion for Gene Function Prediction.- A Cascade Multiple Classifier System for Document Categorization.- Maximum Membership Scale Selection.- An Empirical Study of a Linear Regression Combiner on Multi-class Data Sets.- A Study of Random Linear Oracle Ensembles.- Stacking for Ensembles of Local Experts in Metabonomic Applications.- Boosting Support Vector Machines Successfully.- Invited Papers.- Manifold Learning for Multi-classifier Systems via Ensembles.- When Semi-supervised Learning Meets Ensemble Learning.