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

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Gayar, Neamat El (Επιμελητής έκδοσης), Kittler, Josef (Επιμελητής έκδοσης), Roli, Fabio (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Lecture Notes in Computer Science, 5997
• Θέματα: Computer science.; Computers.; Algorithms.; Database management.; Artificial intelligence.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Information Systems Applications (incl. Internet).; Pattern Recognition.; Algorithm Analysis and Problem Complexity.; Computation by Abstract Devices.; Database Management.

Πίνακας περιεχομένων:

• 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.