Multiple Classifier Systems [electronic resource] : 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings / edited by Neamat Gayar, Josef Kittler, Fabio Roli.
Material type:
TextSeries: Lecture Notes in Computer Science ; 5997Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010Description: X, 328p. 77 illus. online resourceContent type: - text
- computer
- online resource
- 9783642121272
- Computer science
- Computer software
- Database management
- Data mining
- Information systems
- Optical pattern recognition
- Computer Science
- Information Systems Applications (incl.Internet)
- Pattern Recognition
- Algorithm Analysis and Problem Complexity
- Computation by Abstract Devices
- Database Management
- Data Mining and Knowledge Discovery
- 005.7 23
- QA76.76.A65
E-BOOKS
| Home library | Call number | Materials specified | URL | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
| IMSc Library | Link to resource | Available | EBK8971 |
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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