Duin, R.P.W.[Robert P. W.]
* 1981: Dealing with a priori knowledge by fuzzy labels
* 1983: Interactive Image Processing in an Multiuser Environment
* 1984: Spirograph Theory: A Framework for Calculations on Digitized Straight Lines
* 1986: Fast Percentile Filtering
* 1990: Algorithm for Benchmarking an SIMD Pyramid with the Abingdon Cross, An
* 1992: Feedforward neural networks with random weights
* 1993: Evaluation Method for an Automatic Map Interpretation System for Cadastral Maps
* 1994: effective capacity of multilayer feedforward network classifiers, The
* 1995: Evaluation of Intrinsic Dimensionality Estimators, An
* 1995: SMD position measurement by a Kohonen network compared with image processing
* 1996: Combining Classifiers
* 1996: Note on Comparing Classifiers, A
* 1996: On the Nonlinearity of Pattern Classifiers
* 1996: Stabilizing Classifiers for Very Small Sample Sizes
* 1997: Experiments with a Featureless Approach to Pattern Recognition
* 1997: Investigating Redundancy in Feedforward Neural Classifiers
* 1997: Neural network experiences between perceptrons and support vectors
* 1997: Sammons Mapping Using Neural Networks: A Comparison
* 1998: Bagging for Linear Classifiers
* 1998: Expected Classification Error of the Fisher Linear Classifier with Pseudo Inverse Covariance Matrix
* 1998: Neural Network Initialization by Combined Classifiers
* 1998: On Combining Classifiers
* 1998: Relational Discriminant Analysis and its Large Sample Size Problem
* 1999: Applicability of Neural Networks to Non-linear Image Processing, The
* 1999: Regularisation of Linear Classifiers by Adding Redundant Features
* 1999: Weight Set Decorrelating Algorithm for Neural Network Interpretation and Symmetry Breaking, A
* 2000: Adaptive Subspace Map for Texture Segmentation, The
* 2000: Classifiers for Dissimilarity-based Pattern Recognition
* 2000: Classifiers in Almost Empty Spaces
* 2000: Combining multiple classifiers by averaging or by multiplying?
* 2000: Data Description in Subspaces
* 2000: Is Independence Good for Combining Classifiers?
* 2000: Multi-class Linear Feature Extraction by Nonlinear PCA
* 2000: Probabilistic PCA and ICA Subspace Mixture Models for Image Segmentation
* 2000: Role of Subclasses in Machine Diagnostics, The
* 2000: Statistical Pattern Recognition: A Review
* 2001: Decision templates for multiple classifier fusion: An Experimental Comparison
* 2001: Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
* 2001: Segmentation of multi-spectral images using a combined classifier approach
* 2002: combining classifier: to train or not to train?, The
* 2002: Dissimilarity representations allow for building good classifiers
* 2002: economics of classification: error vs. complexity, The
* 2002: note on core research issues for statistical pattern recognition, A
* 2002: Prototype selection for finding efficient representations of dissimilarity data
* 2002: Supervised segmentation of textures in backscatter images
* 2002: Using two-class classifiers for multiclass classification
* 2003: Dissimilarity-based classification of spectra: computational issues
* 2003: Limits on the Majority Vote Accuracy in Classifier Fusion
* 2003: Segmentation of multi-spectral images using the combined classifier approach
* 2004: Almost autonomous training of mixtures of principal component analyzers
* 2004: characterization of classification problems by classifier disagreements, The
* 2004: Classification, parameter estimation and state estimation: An engineering approach using Matlab
* 2004: Dimensionality Reduction by Canonical Contextual Correlation Projections
* 2004: Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
* 2004: Multi-class extensions of the GLDB feature extraction algorithm for spectral data
* 2004: Selective sampling based on the variation in label assignments
* 2005: Dimensionality reduction of image features using the canonical contextual correlation projection
* 2005: Improving the Maximum-Likelihood Co-occurrence Classifier: A Study on Classification of Inhomogeneous Rock Images
* 2005: Tangent Kernel Approach to Illumination-Robust Texture Classification, The
* 2006: Augmented Embedding of Dissimilarity Data into (Pseudo-)Euclidean Spaces
* 2006: Building Road-Sign Classifiers Using a Trainable Similarity Measure
* 2006: Combining Accuracy and Prior Sensitivity for Classifier Design Under Prior Uncertainty
* 2006: Combining Neural Networks for Gait Classification
* 2006: Dissimilarity-based classification for vectorial representations
* 2006: Domain Based LDA and QDA
* 2006: Edge Detection in Hyperspectral Imaging: Multivariate Statistical Approaches
* 2006: Effectiveness of Spectral Band Selection/Extraction Techniques for Spectral Data
* 2006: Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces
* 2006: interaction between classification and reject performance for distance-based reject-option classifiers, The
* 2006: Linear model combining by optimizing the Area under the ROC curve
* 2006: Non-Euclidean or Non-metric Measures Can Be Informative
* 2006: Outlier Detection Using Ball Descriptions with Adjustable Metric
* 2006: Precision-recall operating characteristic (P-ROC) curves in imprecise environments
* 2006: Prototype selection for dissimilarity-based classifiers
* 2006: Recent submissions in linear dimensionality reduction and face recognition
* 2006: Structural Inference of Sensor-Based Measurements
* 2006: study on design of object sorting algorithms in the industrial application using hyperspectral imaging, A
* 2006: Trainable Similarity Measure for Image Classification, A
* 2006: Transforming Strings to Vector Spaces Using Prototype Selection
* 2007: Approximating the multiclass ROC by pairwise analysis
* 2007: Generalizing Dissimilarity Representations Using Feature Lines
* 2007: On Using a Pre-clustering Technique to Optimize LDA-Based Classifiers for Appearance-Based Face Recognition
* 2008: Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis
* 2008: Fast Approach to Improve Classification Performance of ECOC Classification Systems, A
* 2008: Growing a multi-class classifier with a reject option
* 2008: Inexact Graph Comparison Approach in Joint Eigenspace, An
* 2008: Integration of prior knowledge of measurement noise in kernel density classification
* 2008: Learning Curves for the Analysis of Multiple Instance Classifiers
* 2008: Maximizing the area under the ROC curve by pairwise feature combination
* 2008: On Euclidean Corrections for Non-Euclidean Dissimilarities
* 2008: On refining dissimilarity matrices for an improved NN learning
* 2008: Spectral Characterization of Volcanic Earthquakes at Nevado del Ruiz Volcano Using Spectral Band Selection/Extraction Techniques
* 2008: Subclass Problem-Dependent Design for Error-Correcting Output Codes
* 2008: Variance estimation for two-class and multi-class ROC analysis using operating point averaging
* 2009: Clustering Based Method for Edge Detection in Hyperspectral Images, A
* 2009: Clustering-Based Construction of Hidden Markov Models for Generative Kernels
* 2009: Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers, A
* 2009: Component-Based Discriminative Classification for Hidden Markov Models
* 2009: Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction
* 2009: generalization of dissimilarity representations using feature lines and feature planes, A
* 2009: Representation of Chemical Spectral Data for Classification, The
* 2009: Study on Representations for Face Recognition from Thermal Images, A
* 2010: Award winning papers from the 19th International Conference on Pattern Recognition (ICPR)
* 2010: Classification of Volcano Events Observed by Multiple Seismic Stations
* 2010: Classifying Three-way Seismic Volcanic Data by Dissimilarity Representation
* 2010: Feature-Based Dissimilarity Space Classification
* 2010: multi-classifier for grading knee osteoarthritis using gait analysis, A
* 2010: On Improving Dissimilarity-Based Classifications Using a Statistical Similarity Measure
* 2010: Prototype Selection for Dissimilarity Representation by a Genetic Algorithm
* 2010: Random Subspace Method in Text Categorization
* 2010: ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers
* 2010: Spherical embeddings for non-Euclidean dissimilarities
* 2010: Study on Combining Sets of Differently Measured Dissimilarities, A
* 2011: Dissimilarity Representation for Structural Pattern Recognition, The
* 2011: Dissimilarity-Based Classifications in Eigenspaces
* 2011: Dissimilarity-based detection of schizophrenia
* 2011: experimental study of one- and two-level classifier fusion for different sample sizes, An
* 2011: SEDMI: Saliency based edge detection in multispectral images
* 2012: Automated classification of local patches in colon histopathology
* 2012: Combining multi-scale dissimilarities for image classification
* 2012: Continuous Multi-way Shape Measure for Dissimilarity Representation
* 2012: Dipping Phenomenon, The
* 2012: dissimilarity space: Bridging structural and statistical pattern recognition, The
* 2012: Mode Seeking Clustering by KNN and Mean Shift Evaluated
* 2012: On Using Asymmetry Information for Classification in Extended Dissimilarity Spaces
* 2012: study on semi-supervised dissimilarity representation, A
* 2012: Supervised localization of cell nuclei on TMA images
* 2012: Training data selection for cancer detection in multispectral endoscopy images
* 2013: FIDOS: A generalized Fisher based feature extraction method for domain shift
* 2013: Missing Values in Dissimilarity-Based Classification of Multi-way Data
* 2013: Multi-spectral video endoscopy system for the detection of cancerous tissue
* 2013: Multiple-instance learning as a classifier combining problem
* 2013: Towards Cluster-Based Prototype Sets for Classification in the Dissimilarity Space
* 2014: Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance
* 2014: Spherical and Hyperbolic Embeddings of Data
* 2014: Towards Scalable Prototype Selection by Genetic Algorithms with Fast Criteria
* 2015: dissimilarity representation for finding universals from particulars by an anti-essentialist approach, The
* 2016: Compact Representation of Multiscale Dissimilarity Data by Prototype Selection, A
* 2016: Similarity Between Dissimilarities, The
* 2016: Unsupervised Parameter Estimation of Non Linear Scaling for Improved Classification in the Dissimilarity Space
Includes: Duin, R.P.W.[Robert P. W.] Duin, R.P.W. Duin, R.P.W.[Robert P.W.]
140 for Duin, R.P.W.
Duina, D.
* 2001: novel visual landmark matching for a biologically inspired homing, A