Multiple Classifier Systems 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010, Proceedings /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: El Gayar, Neamat (Editor ), Kittler, Josef (Editor ), Roli, Fabio (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2010.
Edición:1st ed. 2010.
Colección:Theoretical Computer Science and General Issues, 5997
Temas:
Artificial intelligence.
Application software.
Pattern recognition systems.
Algorithms.
Computer science.
Database management.
Artificial Intelligence.
Computer and Information Systems Applications.
Automated Pattern Recognition.
Theory of Computation.
Database Management.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.

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