Loog, M.[Marco]
* 2000: Multi-class Linear Feature Extraction by Nonlinear PCA
* 2001: Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
* 2001: On the behavior of spatial critical points under Gaussian blurring
* 2002: Supervised segmentation by iterated contextual pixel classification
* 2003: Gaussian Scale Space from Insufficient Image Information
* 2004: Dimensionality Reduction by Canonical Contextual Correlation Projections
* 2004: Integrating automatic and interactive brain tumor segmentation
* 2004: Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
* 2004: Local Fisher Embedding
* 2004: Pixel Position Regression: Application to Medical Image Segmentation
* 2004: Static posterior probability fusion for signal detection: Applications in the detection of interstitial diseases in chest radiographs
* 2004: Support Blob Machines: The Sparsification of Linear Scale Space
* 2005: Dimensionality reduction of image features using the canonical contextual correlation projection
* 2005: Uncorrelated heteroscedastic LDA based on the weighted pairwise Chernoff criterion
* 2006: Bony Structure Suppression in Chest Radiographs
* 2006: Conditional Linear Discriminant Analysis
* 2006: Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis
* 2006: Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction
* 2006: Generic Blind Source Separation Using Second-Order Local Statistics
* 2006: Local Discriminant Analysis
* 2006: Recent submissions in linear dimensionality reduction and face recognition
* 2006: Segmentation of the Posterior Ribs in Chest Radiographs Using Iterated Contextual Pixel Classification
* 2007: Blur Invariant Image Priors
* 2007: Generic Maximum Likely Scale Selection
* 2007: Jet Metric, The
* 2007: On an alternative formulation of the Fisher criterion that overcomes the small sample problem
* 2008: Automated Effect-Specific Mammographic Pattern Measures
* 2008: Efficient Segmentation by Sparse Pixel Classification
* 2008: On Distributional Assumptions and Whitened Cosine Similarities
* 2008: Second Order Structure of Scale-Space Measurements
* 2009: Bicycle chain shape models
* 2009: Clustering Based Method for Edge Detection in Hyperspectral Images, A
* 2009: Dense iterative contextual pixel classification using Kriging
* 2010: Feature-Based Dissimilarity Space Classification
* 2010: Improbability of Harris Interest Points, The
* 2010: Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy
* 2010: Stratified Generalized Procrustes Analysis
* 2010: Using Multiscale Spectra in Regularizing Covariance Matrices for Hyperspectral Image Classification
* 2011: Dissimilarity-based detection of schizophrenia
* 2011: Forming Different-Complexity Covariance-Model Subspaces through Piecewise-Constant Spectra for Hyperspectral Image Classification
* 2011: Information theoretic preattentive saliency: A closed-form solution
* 2011: On Combining Computer-Aided Detection Systems
* 2011: SEDMI: Saliency based edge detection in multispectral images
* 2011: Supervised Scale-Invariant Segmentation (and Detection)
* 2012: Automated classification of local patches in colon histopathology
* 2012: Class-Dependent Dissimilarity Measures for Multiple Instance Learning
* 2012: Combining multi-scale dissimilarities for image classification
* 2012: Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis
* 2012: Dipping Phenomenon, The
* 2012: Does one rotten apple spoil the whole barrel?
* 2012: Improving cross-validation based classifier selection using meta-learning
* 2012: Metric learning by directly minimizing the k-NN training error
* 2012: Mode Seeking Clustering by KNN and Mean Shift Evaluated
* 2012: Scale selection for supervised image segmentation
* 2012: Scale-invariant sampling for supervised image segmentation
* 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: Multi-spectral video endoscopy system for the detection of cancerous tissue
* 2013: Multiple-instance learning as a classifier combining problem
* 2013: Stratified Generalized Procrustes Analysis
* 2014: Classification of COPD with Multiple Instance Learning
* 2014: Implicitly Constrained Semi-supervised Linear Discriminant Analysis
* 2014: Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance
* 2014: Network-Guided Group Feature Selection for Classification of Autism Spectrum Disorder
* 2014: Optic Nerve Head Detection via Group Correlations in Multi-orientation Transforms
* 2014: Semi-supervised linear discriminant analysis through moment-constraint parameter estimation
* 2015: Multiple instance learning with bag dissimilarities
* 2015: On classification with bags, groups and sets
* 2015: Single- vs. multiple-instance classification
* 2015: Training of Templates for Object Recognition in Invertible Orientation Scores: Application to Optic Nerve Head Detection in Retinal Images
* 2016: Active learning using uncertainty information
* 2016: Compact Representation of Multiscale Dissimilarity Data by Prototype Selection, A
* 2016: Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification
* 2016: empirical investigation into the inconsistency of sequential active learning, An
* 2016: Learning Algorithms for Digital Reconstruction of Van Gogh's Drawings
* 2016: On regularization parameter estimation under covariate shift
* 2016: Optimistic semi-supervised least squares classification
* 2016: Peaking Phenomenon in Semi-supervised Learning, The
* 2016: soft-labeled self-training approach, A
* 2016: Weighted K-Nearest Neighbor revisited
* 2017: Editorial of the Special Issue on Multi-instance Learning in Pattern Recognition and Vision
* 2017: Robust semi-supervised least squares classification by implicit constraints
* 2018: benchmark and comparison of active learning for logistic regression, A
* 2018: Effects of sampling skewness of the importance-weighted risk estimator on model selection.
* 2018: Gradient Descent for Gaussian Processes Variance Reduction
* 2018: Protein Remote Homology Detection Using Dissimilarity-Based Multiple Instance Learning
* 2018: Template Matching via Densities on the Roto-Translation Group
* 2018: variance maximization criterion for active learning, A
* 2019: dissimilarity-based multiple instance learning approach for protein remote homology detection, A
* 2019: Gaussian process variance reduction by location selection
* 2019: Multi-scale convolutional neural network for pixel-wise reconstruction of Van Gogh's drawings
* 2019: Single shot active learning using pseudo annotators
* 2021: Bayesian Active Learning for Maximal Information Gain on Model Parameters
* 2021: Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory
* 2021: Respecting Domain Relations: Hypothesis Invariance for Domain Generalization
* 2021: Review of Domain Adaptation without Target Labels, A
* 2021: Robust domain-adaptive discriminant analysis
* 2022: Enhancing Classifier Conservativeness and Robustness by Polynomiality
* 2022: Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues
* 2022: To Actively Initialize Active Learning
* 2023: Improved Generalization in Semi-Supervised Learning: A Survey of Theoretical Results
* 2023: Shape of Learning Curves: A Review, The
Includes: Loog, M.[Marco] Loog, M.
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