Multiple Classifier Systems: 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010, Proceedings: Lecture Notes in Computer Science, cartea 5997
Editat de Neamat El Gayar, Josef Kittler, Fabio Rolien Limba Engleză Paperback – 25 mar 2010
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Specificații
ISBN-13: 9783642121265
ISBN-10: 3642121268
Pagini: 344
Ilustrații: X, 328 p. 77 illus.
Greutate: 0.52 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642121268
Pagini: 344
Ilustrații: X, 328 p. 77 illus.
Greutate: 0.52 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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 toHandwritten 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.
Caracteristici
Fast track conference proceedings State of the art papers in multiple classifier systems Up to date research