Decoding perceptual thresholds from MEG/EEG, 2014 International Workshop on Pattern Recognition in Neuroimaging, p.0, 2014. ,
DOI : 10.1109/PRNI.2014.6858510
URL : https://hal.archives-ouvertes.fr/hal-01032909
A perceptual-toconceptual gradient of word coding along the ventral path, 4th International Workshop on Pattern Recognition in Neuroimaging, pp.3-6, 2014. ,
New approaches to support vector ordinal regression, Proceedings of the 22nd international conference on Machine learning , ICML '05, 2005. ,
DOI : 10.1145/1102351.1102370
Support Vector Ordinal Regression, Neural Computation, vol.47, issue.3, pp.792-815, 2001. ,
DOI : 10.1162/089976601300014493
Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, 1995. ,
DOI : 10.1002/hbm.460020402
Large margin rank boundaries for ordinal regression, pp.115-132, 1998. ,
Analyses of regional-average activation and multivoxel pattern information tell complementary stories, Neuropsychologia, vol.50, issue.4, pp.1-9, 2011. ,
DOI : 10.1016/j.neuropsychologia.2011.11.007
Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, 2002. ,
DOI : 10.1145/775047.775067
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.3161
Identifying natural images from human brain activity, Nature, vol.79, issue.7185, pp.352-357, 2008. ,
DOI : 10.1038/nature06713
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556484
Multicategory Support Vector Machines, Journal of the American Statistical Association, vol.99, issue.465, pp.9967-81, 2004. ,
DOI : 10.1198/016214504000000098
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.1879
Ordinal Regression by Extended Binary Classiication, Advances in Neural Information Processing Systems (NIPS), 2007. ,
Large-margin thresholded ensembles for ordinal regression: Theory and practice, Algorithmic Learning Theory, pp.319-333, 2006. ,
Learning to Rank for Information Retrieval, Foundations and Trends?? in Information Retrieval, vol.3, issue.3, pp.225-331, 2009. ,
DOI : 10.1561/1500000016
Regression Models for Ordinal Data, Journal of the Royal Statistical Society, vol.42, issue.2, pp.109-142, 1980. ,
Bayesian Reconstruction of Natural Images from Human Brain Activity, Neuron, vol.63, issue.6, pp.902-915, 2009. ,
DOI : 10.1016/j.neuron.2009.09.006
Learning to Rank from Medical Imaging Data, Machine Learning in Medical Imaging, pp.234-241, 2012. ,
DOI : 10.1007/978-3-642-35428-1_29
URL : https://hal.archives-ouvertes.fr/hal-00717990
Data-driven HRF estimation for encoding and decoding models, NeuroImage, vol.104, pp.209-220, 2014. ,
DOI : 10.1016/j.neuroimage.2014.09.060
URL : https://hal.archives-ouvertes.fr/hal-00952554
Classiication Calibration Dimension for General Multiclass Losses, Advances in Neural Information Processing Systems, pp.1-15, 2012. ,
Multisection cerebral blood ow mr imaging with continuous arterial spin labeling, Radiology, vol.208, issue.2, pp.410-416, 1998. ,
Roberto Lent, and Suzana Herculano-Houzel. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain, The Journal of Comparative Neurology, issue.5, pp.513532-541, 2009. ,
Enhanced phase regression with savitzky-golay ltering for high-resolution bold fmri, Human Brain Mapping, vol.35, issue.8, pp.3832-3840, 2014. ,
A consistent relationship between local white matter architecture and functional specialisation in medial frontal cortex, Neuroimage, vol.30, issue.1, pp.220-227, 2006. ,
Learning the value of information in an uncertain world, Nature neuroscience, vol.10, issue.9, pp.1214-1221, 2007. ,
A perceptual-toconceptual gradient of word coding along the ventral path, In Pattern Recognition in Neuroimaging, 2014. ,
Arterial spin labeling (ASL) fMRI: Advantages, theoretical constrains and experimental challenges in neurosciences, International Journal of Biomedical Imaging, 2012. ,
Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1, The Journal of Neuroscience, vol.16, issue.13, pp.4207-4221, 1996. ,
Statistical methods of estimation and inference for functional MR image analysis, Magnetic Resonance in Medicine, vol.13, issue.2, pp.261-277, 1996. ,
DOI : 10.1002/mrm.1910350219
Variable density sampling with continuous trajectories . application to MRI, SIAM J. Imaging Science, vol.7, issue.4, p.2014 ,
Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, vol.39, issue.368, pp.829-836, 1979. ,
DOI : 10.1214/aos/1176343886
Parametric analysis of fMRI data using linear systems methods, NeuroImage, vol.6, issue.2, pp.93-103, 1997. ,
Attention during natural vision warps semantic representation across the human brain, Nature Neuroscience, vol.16, issue.6, pp.763-770, 2013. ,
DOI : 10.1038/nn.3381
Selective averaging of rapidly presented individual trials using fMRI, Human Brain Mapping, vol.5, issue.5, pp.329-369, 1997. ,
Technical aspects and utility of fMRI using BOLD and ASL, Clinical Neurophysiology, vol.113, issue.5, pp.621-634, 2002. ,
Tissue specific perfusion imaging using arterial spin labeling, NMR in Biomedicine, vol.91, issue.1-2, pp.75-82, 1994. ,
DOI : 10.1002/nbm.1940070112
Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, 1995. ,
DOI : 10.1002/hbm.460020402
The cognitive neurosciences, 2004. ,
Deconvolution of impulse response in event-related BOLD fMRI, NeuroImage, vol.9, issue.4, pp.416-429, 1999. ,
Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision, 2009. ,
URL : https://hal.archives-ouvertes.fr/tel-00426852
A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie, Scientific Data, vol.8, 2014. ,
DOI : 10.1038/sdata.2014.3
Functional-Anatomical Validation and Individual Variation of Diffusion Tractography-based Segmentation of the Human Thalamus, Cerebral Cortex, vol.15, issue.1, pp.31-39, 2005. ,
DOI : 10.1093/cercor/bhh105
Nonlinear Regression of Functional MRI Data: An Item Recognition Task Study, NeuroImage, vol.12, issue.2, pp.173-183, 2000. ,
DOI : 10.1006/nimg.2000.0604
Modeling the hemodynamic response function in fmri: eeciency, bias and mis-modeling, Neuroimage, vol.45, issue.1, pp.187-198, 2009. ,
Sparse MRI: The application of compressed sensing for rapid MR imaging, Magnetic Resonance in Medicine, vol.170, issue.6, pp.1182-1195, 2007. ,
DOI : 10.1002/mrm.21391
Understanding the visual cortex by using classiication techniques, 2010. ,
Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proceedings of the National Academy of Sciences, vol.87, issue.24, pp.9868-9872, 1990. ,
DOI : 10.1073/pnas.87.24.9868
Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields, Magnetic Resonance in Medicine, vol.45, issue.1, pp.68-78, 1990. ,
DOI : 10.1002/mrm.1910140108
How the Brain Translates Money into Force: A Neuroimaging Study of Subliminal Motivation, Science, vol.316, issue.5826, pp.316-904, 2007. ,
DOI : 10.1126/science.1140459
Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high eld, BBA) - General Subjects, pp.265-270, 1982. ,
Accounting for nonlinear BOLD eeects in fMRI: parameter estimates and a model for prediction in rapid event-related studies, NeuroImage, vol.25, issue.1, pp.206-218, 2005. ,
Magnetic resonance imaging of perfusion using spin inversion of arterial water, Proceedings of the National Academy of Sciences, pp.212-216, 1992. ,
Dynamics and nonlinearities of the BOLD response at very short stimulus durations, Proceedings of the International School on Magnetic Resonance and Brain Function, pp.853-862, 2008. ,
DOI : 10.1016/j.mri.2008.01.008
Compressed sensing fmri using gradient-recalled echo and EPI sequences ISSN 1053-8119. 3.2.1 Supervised Learning, NeuroImage, issue.0, pp.92312-321, 2014. ,
A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010. ,
DOI : 10.1214/09-SS054
URL : https://hal.archives-ouvertes.fr/hal-00407906
Structured Sparsity Models for Brain Decoding from fMRI Data, 2012 Second International Workshop on Pattern Recognition in NeuroImaging, pp.5-8, 2012. ,
DOI : 10.1109/PRNI.2012.31
Pattern recognition and machine learning, 2006. ,
Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm, Psychometrika, vol.46, issue.4, pp.443-459, 1981. ,
DOI : 10.1007/BF02293801
A perceptual-toconceptual gradient of word coding along the ventral path, In Pattern Recognition in Neuroimaging, 2014. ,
A training algorithm for optimal margin classiiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT '92, pp.144-152, 1992. ,
Comparison of classiier methods: a case study in handwritten digit recognition, International Conference on Pattern Recognition, pp.77-77, 1994. ,
An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.161-168, 2006. ,
DOI : 10.1145/1143844.1143865
Statistical Inference., Biometrics, vol.49, issue.1, 2002. ,
DOI : 10.2307/2532634
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration, SIAM Journal on Scientific Computing, vol.20, issue.6, pp.1964-1977, 1999. ,
DOI : 10.1137/S1064827596299767
Atomic Decomposition by Basis Pursuit, SIAM Review, vol.43, issue.1, pp.129-159, 2001. ,
DOI : 10.1137/S003614450037906X
Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995. ,
DOI : 10.1007/BF00994018
Functional magnetic resonance imaging (fMRI) " brain reading " : detecting and classifying distributed patterns of fMRI activity in human visual cortex, NeuroImage, vol.19, issue.2, pp.261-270, 2003. ,
Inferring behavior from functional brain images, Nature Neuroscience, vol.388, issue.7, pp.549-549, 1998. ,
DOI : 10.1038/2785
URL : https://hal.archives-ouvertes.fr/hal-00349936
Benchmarking solvers for tv-l1 leastsquares and logistic regression in brain imaging, Pattern Recoginition in Neuroimaging (PRNI), 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-00991743
Adapting to unknown smoothness via wavelet shrinkage, Journal of the american statistical association, vol.90, issue.432, pp.1200-1224, 1995. ,
Support vector regression machines Advances in neural information processing systems, pp.155-161, 1997. ,
Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI, 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.1-4, 2014. ,
DOI : 10.1109/PRNI.2014.6858517
URL : https://hal.archives-ouvertes.fr/cea-01016145
Agnostic Learning of Monomials by Halfspaces Is Hard, SIAM Journal on Computing, vol.41, issue.6, pp.1558-1590, 2012. ,
DOI : 10.1137/120865094
Statistical methods for research workers, 1925. ,
"Who" Is Saying "What"? Brain-Based Decoding of Human Voice and Speech, Science, vol.322, issue.5903, pp.322970-973, 2008. ,
DOI : 10.1126/science.1164318
Resampling fMRI time series, NeuroImage, vol.25, issue.3, pp.859-867, 2005. ,
DOI : 10.1016/j.neuroimage.2004.11.046
Assessing the significance of focal activations using their spatial extent, Human Brain Mapping, vol.12, issue.3, pp.210-220, 1994. ,
DOI : 10.1002/hbm.460010306
The Predictive Sample Reuse Method with Applications, Journal of the American Statistical Association, vol.36, issue.2, pp.320-328, 1975. ,
DOI : 10.1080/01621459.1975.10479865
Identifying Predictive Regions from fMRI with TV-L1 Prior, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013. ,
DOI : 10.1109/PRNI.2013.14
URL : https://hal.archives-ouvertes.fr/hal-00839984
Decoding reveals the contents of visual working memory in early visual areas, Nature, vol.458, issue.7238, pp.632-635, 2009. ,
Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Science, vol.293, issue.5539, pp.2932425-2430, 2001. ,
DOI : 10.1126/science.1063736
Predicting the orientation of invisible stimuli from activity in human primary visual cortex, Nature Neuroscience, vol.268, issue.5, pp.686-691, 2005. ,
DOI : 10.1162/089892900562561
Reading Hidden Intentions in the Human Brain, Current Biology, vol.17, issue.4, pp.323-328, 2007. ,
DOI : 10.1016/j.cub.2006.11.072
Bootstrapping Phylogenetic Trees: Theory and Methods, Statistical Science, vol.18, issue.2, pp.241-255, 2003. ,
DOI : 10.1214/ss/1063994979
Decoding the visual and subjective contents of the human brain, Nature Neuroscience, vol.15, issue.5, pp.679-685, 2005. ,
DOI : 10.1097/00004728-199801000-00027
Identifying natural images from human brain activity, Nature, vol.79, issue.7185, pp.352-357, 2008. ,
DOI : 10.1038/nature06713
STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS, Applied Artificial Intelligence, vol.9, issue.3, pp.289-333, 1995. ,
DOI : 10.1080/08839519508945477
Support vector machines for temporal classification of block design fMRI data, NeuroImage, vol.26, issue.2, pp.317-329, 2005. ,
DOI : 10.1016/j.neuroimage.2005.01.048
An evaluation of thresholding techniques in fmri analysis, NeuroImage, vol.22, issue.1, pp.95-108, 2004. ,
Total Variation Regularization for fMRI-Based Prediction of Behavior, IEEE Transactions on Medical Imaging, vol.30, issue.7, pp.1328-1340, 2011. ,
DOI : 10.1109/TMI.2011.2113378
Predicting Human Brain Activity Associated with the Meanings of Nouns, Science, vol.320, issue.5880, pp.320-1191, 2008. ,
DOI : 10.1126/science.1152876
Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders, Neuron, vol.60, issue.5, pp.915-929, 2008. ,
DOI : 10.1016/j.neuron.2008.11.004
Data analysis, including statistics, 1968. ,
Bayesian Reconstruction of Natural Images from Human Brain Activity, Neuron, vol.63, issue.6, pp.902-915, 2009. ,
DOI : 10.1016/j.neuron.2009.09.006
Encoding and decoding in fMRI, NeuroImage, vol.56, issue.2, pp.400-410, 2011. ,
DOI : 10.1016/j.neuroimage.2010.07.073
A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes, NeuroImage, vol.105, issue.0, 2014. ,
DOI : 10.1016/j.neuroimage.2014.10.018
Multiple testing corrections, nonparametric methods, and random eld theory, NeuroImage, vol.62, issue.2, pp.811-815, 2012. ,
Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology, vol.21, issue.19, pp.1641-1646, 2011. ,
DOI : 10.1016/j.cub.2011.08.031
Fast reproducible identification and large-scale databasing of individual functional cognitive networks, BMC Neuroscience, vol.8, issue.1, p.91, 2007. ,
DOI : 10.1186/1471-2202-8-91
URL : https://hal.archives-ouvertes.fr/hal-00784462
Mathematical statistics and data analysis, Cengage Learning, 2006. ,
Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992. ,
DOI : 10.1016/0167-2789(92)90242-F
Kernel methods for pattern analysis, 2004. ,
DOI : 10.1017/CBO9780511809682
Brain decoding: Reading minds, Nature, vol.502, issue.7472, 2013. ,
DOI : 10.1038/502428a
Comparative study of svm methods combined with voxel selection for object category classiication on fmri data, PLoS ONE, vol.6, issue.2, pp.2-2011 ,
Unconscious determinants of free decisions in the human brain, Nature Neuroscience, vol.8, issue.5, pp.543-545, 2008. ,
DOI : 10.1038/nrn1931
Sound Categories Are Represented as Distributed Patterns in the Human Auditory Cortex, Current Biology, vol.19, issue.6, pp.498-502, 2009. ,
DOI : 10.1016/j.cub.2009.01.066
Estimation of the mean of a multivariate normal distribution. The annals of Statistics, pp.1135-1151, 1981. ,
Asymptotics for and against cross-validation, Biometrika, vol.64, issue.1, pp.29-35, 1977. ,
DOI : 10.1093/biomet/64.1.29
The probable error of a mean, Biometrika, pp.1-25, 1908. ,
Inverse retinotopy: Inferring the visual content of images from brain activation patterns, NeuroImage, vol.33, issue.4, pp.1104-1120, 2006. ,
DOI : 10.1016/j.neuroimage.2006.06.062
URL : https://hal.archives-ouvertes.fr/hal-00349668
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996. ,
Solutions of ill-posed problems (translated from the russian), 1977. ,
Multiple comparison procedures. Number 89, Sage, 1993. ,
Teoriya raspoznavaniya obrazov. statisticheskie problemy obucheniya (theory of pattern recognition. statistical problems of learning, 1974. ,
Resampling-based multiple testing: Examples and methods for p-value adjustment, 1993. ,
A Three-Dimensional Statistical Analysis for CBF Activation Studies in Human Brain, Journal of Cerebral Blood Flow & Metabolism, vol.251, issue.6, pp.900-918, 1992. ,
DOI : 10.1038/jcbfm.1992.127
Statistical Analysis of Some Multi-Category Large Margin Classiication Methods, Journal of Machine Learning Research, vol.5, pp.1225-1251, 2004. ,
Regularization and variable selection via the elastic net, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.2, pp.301-320, 2005. ,
DOI : 10.1073/pnas.201162998
The variability of human, BOLD hemodynamic responses, NeuroImage, vol.8, issue.4, pp.360-369, 1998. ,
Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.211-215, 2013. ,
DOI : 10.1109/PRNI.2013.61
URL : https://hal.archives-ouvertes.fr/hal-00854621
Group-level impacts of within- and between-subject hemodynamic variability in fMRI, NeuroImage, vol.82, pp.433-448, 2013. ,
DOI : 10.1016/j.neuroimage.2013.05.100
URL : https://hal.archives-ouvertes.fr/hal-00854481
Multi-subject Bayesian Joint Detection and Estimation in fMRI, 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.1-4, 2014. ,
DOI : 10.1109/PRNI.2014.6858508
Decoding perceptual thresholds from MEG/EEG, 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014. ,
DOI : 10.1109/PRNI.2014.6858510
URL : https://hal.archives-ouvertes.fr/hal-01032909
The impact of temporal regularization on estimates of the BOLD hemodynamic response function: A comparative analysis, NeuroImage, vol.40, issue.4, pp.1606-1624, 2008. ,
DOI : 10.1016/j.neuroimage.2008.01.011
Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.180-188, 2012. ,
DOI : 10.1007/978-3-642-33454-2_23
URL : https://hal.archives-ouvertes.fr/hal-00859388
Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment, IEEE Transactions on Medical Imaging, vol.22, issue.10, pp.1235-51, 2003. ,
DOI : 10.1109/TMI.2003.817759
URL : https://hal.archives-ouvertes.fr/cea-00333694
Development of hemodynamic responses and functional connectivity in rat somatosensory cortex, Nature neuroscience, vol.11, issue.1, pp.72-79, 2007. ,
Optimal experimental design for event-related fMRI, Human Brain Mapping, vol.6, issue.2-3, pp.109-123, 1999. ,
DOI : 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W
A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies, NeuroImage, vol.98, pp.61-72, 2014. ,
DOI : 10.1016/j.neuroimage.2014.04.052
Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, vol.26, issue.4, 1995. ,
DOI : 10.1002/hbm.460020402
Nonlinear event-related responses in fMRI, Magnetic Resonance in Medicine, vol.4, issue.1, pp.41-52, 1998. ,
DOI : 10.1002/mrm.1910390109
Deconvolution of impulse response in event-related BOLD fMRI, NeuroImage, vol.9, issue.4, pp.416-445, 1999. ,
Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter, Technometrics, vol.5, issue.2, pp.215-223, 1979. ,
DOI : 10.1080/03610927508827223
Modeling the hemodynamic response in fMRI using smooth FIR filters, IEEE Transactions on Medical Imaging, vol.19, issue.12, pp.1188-201, 2000. ,
DOI : 10.1109/42.897811
Variation of BOLD hemodynamic responses across subjects and brain regions and their eeects on statistical analyses, NeuroImage, vol.21, issue.4, pp.1639-51, 2004. ,
Topics in matrix analysis, 1991. ,
Identifying natural images from human brain activity, Nature, vol.79, issue.7185, pp.352-357, 2008. ,
DOI : 10.1038/nature06713
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556484
fMRI of human visual areas in response to natural images, 2011. ,
A NEW MEASURE OF RANK CORRELATION, Biometrika, vol.30, issue.1-2, pp.81-93, 1938. ,
DOI : 10.1093/biomet/30.1-2.81
A mixed L2 norm regularized HRF estimation method for rapid event-related fMRI experiments. Computational and mathematical methods in medicine, p.643129, 2013. ,
Validity and power in hemodynamic response modeling: A comparison study and a new approach, Hum Brain Mapp, vol.28, issue.8, pp.764-784, 2007. ,
On the limited memory BFGS method for large scale optimization, Mathematical programming, vol.45, issue.1-3, pp.503-528, 1989. ,
Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution, IEEE Transactions on Signal Processing, vol.53, issue.9, pp.3488-3502, 2005. ,
DOI : 10.1109/TSP.2005.853303
Bayesian deconvolution fMRI data using bilinear dynamical systems, NeuroImage, vol.42, issue.4, pp.1381-96, 2008. ,
DOI : 10.1016/j.neuroimage.2008.05.052
Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information, Human Brain Mapping, vol.2, issue.1, pp.1-17, 2003. ,
DOI : 10.1002/hbm.10100
URL : https://hal.archives-ouvertes.fr/cea-00333748
Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses, NeuroImage, vol.59, issue.3, pp.2636-2679, 2012. ,
DOI : 10.1016/j.neuroimage.2011.08.076
Bayesian Reconstruction of Natural Images from Human Brain Activity, Neuron, vol.63, issue.6, pp.902-915, 2009. ,
DOI : 10.1016/j.neuron.2009.09.006
Newton-type minimization via the lanczos method, SIAM Journal on Numerical Analysis, vol.21, issue.4, pp.770-788, 1984. ,
Numerical optimization, series in operations research and nancial engineering, 2006. ,
Learning to Rank from Medical Imaging Data, Third International Workshop on Machine Learning in Medical Imaging -MLMI 2012, 2012. ,
DOI : 10.1007/978-3-642-35428-1_29
URL : https://hal.archives-ouvertes.fr/hal-00717990
Handbook of Functional MRI Data Analysis, 2011. ,
DOI : 10.1017/CBO9780511895029
The general linear model and fMRI: Does love last forever?, NeuroImage, vol.62, issue.2, pp.871-80, 2012. ,
DOI : 10.1016/j.neuroimage.2012.01.133
Unconditional Non-Asymptotic One-Sided Tests for Independent Binomial Proportions When the Interest Lies in Showing Non-Inferiority and/or Superiority, Biometrical Journal, vol.41, issue.2, pp.149-170, 1999. ,
DOI : 10.1002/(SICI)1521-4036(199905)41:2<149::AID-BIMJ149>3.0.CO;2-E
Smoothing and Differentiation of Data by Simplified Least Squares Procedures., Analytical Chemistry, vol.36, issue.8, pp.1627-1639, 1964. ,
DOI : 10.1021/ac60214a047
Late onset of anterior prefrontal activity during true and false recognition: An event-related fMRI study, NeuroImage, vol.6, issue.4, pp.259-269, 1997. ,
Linear reconstruction of perceived images from human brain activity, NeuroImage, vol.83, pp.951-961, 2013. ,
DOI : 10.1016/j.neuroimage.2013.07.043
The neural basis of loss aversion in decisionmaking under risk, Science, issue.5811, pp.315515-315523, 2007. ,
Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs, NeuroImage, vol.62, issue.3, pp.1429-1467, 2012. ,
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series, IEEE Transactions on Medical Imaging, vol.29, issue.4, pp.1059-1074, 2010. ,
DOI : 10.1109/TMI.2010.2042064
URL : https://hal.archives-ouvertes.fr/cea-00470594
Multiscale adaptive smoothing models for the hemodynamic response function in fMRI, The Annals of Applied Statistics, vol.7, issue.2, pp.904-935, 2013. ,
DOI : 10.1214/12-AOAS609SUPP
Constrained linear basis sets for HRF modelling using variational bayes, NeuroImage, vol.21, issue.4, pp.1748-61, 2004. ,
Nonparametric inference of the hemodynamic response using multi-subject fMRI data, NeuroImage, vol.63, issue.3, pp.1754-65, 2012. ,
DOI : 10.1016/j.neuroimage.2012.08.014
A semi-parametric model of the hemodynamic response for multi-subject fMRI data, NeuroImage, vol.75, pp.136-181, 2013. ,
DOI : 10.1016/j.neuroimage.2013.02.048
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software, vol.23, issue.4, pp.550-560, 1997. ,
DOI : 10.1145/279232.279236
Least Absolute Deviations Curve-Fitting, SIAM Journal on Scientific and Statistical Computing, vol.1, issue.2, pp.290-301, 1980. ,
DOI : 10.1137/0901019
Least absolute deviations: Theory, applications and algorithms, Birkhiiuser, 1983. ,
A perceptual-toconceptual gradient of word coding along the ventral path, 4th International Workshop on Pattern Recognition in Neuroimaging, pp.3-6, 2014. ,
Learning to Rank with Nonsmooth Cost Functions, Machine Learning, vol.19, issue.17, pp.193-200, 2007. ,
On the ( Non-) existence of Convex , Calibrated Surrogate Losses for Ranking, Advances in Neural Information Processing Systems 2012, pp.1-9, 2012. ,
Measuring the Performance of Ordinal Classiication, International Journal of Pattern Recognition and Artiicial Intelligence, vol.25, issue.08, pp.1173-1195, 2011. ,
Traitement des Structures Syntaxiques dans le langage et dans la musique, 2012. ,
A uniied view on loss functions in learning to rank, 2009. ,
Gaussian Processes for Ordinal Regression, Journal of Machine Learning Research, vol.6, pp.1-24, 2005. ,
New approaches to support vector ordinal regression, Proceedings of the 22nd international conference on Machine learning , ICML '05, 2005. ,
DOI : 10.1145/1102351.1102370
Support Vector Ordinal Regression, Neural Computation, vol.47, issue.3, pp.792-815, 2001. ,
DOI : 10.1162/089976601300014493
Functional magnetic resonance imaging (fMRI) " brain reading " : detecting and classifying distributed patterns of fMRI activity in human visual cortex, NeuroImage, vol.19, issue.2, pp.261-270, 2003. ,
Log-linear models for label ranking, Advances in Neural Information Processing Systems 16, pp.497-504, 2004. ,
On the Consistency of Ranking Algorithms, Proceedings of the 27th International Conference on Machine Learning, 2010. ,
Liblinear: A library for large linear classiication, The Journal of Machine Learning Research, vol.9, pp.1871-1874, 2008. ,
A Simple Approach to Ordinal Classiication, ECML '01: Proceedings of the 12th European Conference on Machine Learning, 2001. ,
An eecient boosting algorithm for combining preferences . The journal of machine learning research, pp.933-969, 2003. ,
The numeric rating scale for clinical pain measurement: A ratio measure? Pain Practice, pp.310-316, 2003. ,
Large margin rank boundaries for ordinal regression, pp.115-132, 2000. ,
Large-scale linear support vector regression, The Journal of Machine Learning Research, vol.13, issue.1, pp.3323-3348, 2012. ,
Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, 2002. ,
DOI : 10.1145/775047.775067
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.3161
Training linear SVMs in linear time, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.217-226, 2006. ,
DOI : 10.1145/1150402.1150429
Support vector machines for temporal classification of block design fMRI data, NeuroImage, vol.26, issue.2, pp.317-329, 2005. ,
DOI : 10.1016/j.neuroimage.2005.01.048
Multicategory Support Vector Machines, Journal of the American Statistical Association, vol.99, issue.465, pp.9967-81, 2004. ,
DOI : 10.1198/016214504000000098
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.1879
Trust region newton method for logistic regression, The Journal of Machine Learning Research, vol.9, pp.627-650, 2008. ,
Large-margin thresholded ensembles for ordinal regression: Theory and practice, Algorithmic Learning Theory, pp.319-333, 2006. ,
Regression Models for Ordinal Data, Journal of the Royal Statistical Society, vol.42, issue.2, pp.109-142, 1980. ,
Ways toward an early diagnosis in alzheimer's disease: The alzheimer's disease neuroimaging initiative (adni) Alzheimer's & Dementia, pp.55-66, 2005. ,
The minimum sum of absolute errors regression: A state of the art survey, International Statistical Review/Revue Internationale de Statistique, pp.317-326, 1982. ,
Encoding and decoding in fMRI, NeuroImage, vol.56, issue.2, pp.400-410, 2011. ,
DOI : 10.1016/j.neuroimage.2010.07.073
Cortical representation of the constituent structure of sentences, Proceedings of the National Academy of Sciences, pp.2522-2527, 2011. ,
DOI : 10.1073/pnas.1018711108
Numerical optimizers for logistic regression. http://fa.bianp.net/blog/2013/ numerical-optimizers-for-logistic-regression, pp.2014-2025, 2013. ,
Learning to Rank from Medical Imaging Data, Machine Learning in Medical Imaging, pp.234-241, 2012. ,
DOI : 10.1007/978-3-642-35428-1_29
URL : https://hal.archives-ouvertes.fr/hal-00717990
Loss Functions for Preference Levels : Regression with Discrete Ordered Labels, Proceedings of the IJCAI Multidisciplinary Workshop on Advances in Preference Handling, 2005. ,
Learning to order things, Advances in Neural Information Processing Systems 10: Proceedings of the 1997 Conference, p.451, 1998. ,
Large Scale Learning to Rank, NIPS 2009 Workshop on Advances in Ranking, pp.1-6, 2009. ,
Ranking with large margin principle : Two approaches, Advances in Neural Information Processing Systems (NIPS), 2003. ,
Comparative study of svm methods combined with voxel selection for object category classiication on fmri data, PLoS ONE, vol.6, issue.2, pp.2-2011 ,
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis, Bioinformatics, vol.21, issue.5, pp.631-643, 2005. ,
DOI : 10.1093/bioinformatics/bti033
Inverse retinotopy: Inferring the visual content of images from brain activation patterns, NeuroImage, vol.33, issue.4, pp.1104-1120, 2006. ,
DOI : 10.1016/j.neuroimage.2006.06.062
URL : https://hal.archives-ouvertes.fr/hal-00349668
EEcient ranking from pairwise comparisons, Proceedings of the 30th International Conference on Machine Learning, pp.109-117, 2013. ,
Variable selection for the multicategory SVM via adaptive sup-norm regularization, Electronic Journal of Statistics, vol.2, issue.0, pp.149-167, 2008. ,
DOI : 10.1214/08-EJS122
Bayesian multicategory support vector machines, In In Uncertainty in Artiicial Intelligence, 2006. ,
Regression models for ordinal responses: a review of methods and applications, International journal of epidemiology, vol.26, issue.6, pp.1323-1333, 1997. ,
Ordinal regression models for epidemiologic data, American Journal of Epidemiology, vol.129, issue.1, pp.191-204, 1989. ,
Convexity, classiication, and risk bounds, Journal of the American Statistical Association, vol.101, issue.473, pp.138-156, 2003. ,
On the difficulty of approximately maximizing agreements, Journal of Computer and System Sciences, vol.66, issue.3, pp.496-514, 2003. ,
DOI : 10.1016/S0022-0000(03)00038-2
Convex optimization, 2004. ,
Gaussian processes for ordinal regression, Journal of Machine Learning Research, vol.6, pp.1-24, 2004. ,
New approaches to support vector ordinal regression, Proceedings of the 22nd international conference on Machine learning , ICML '05, 2005. ,
DOI : 10.1145/1102351.1102370
Support Vector Ordinal Regression, Neural Computation, vol.47, issue.3, pp.792-815, 2001. ,
DOI : 10.1162/089976601300014493
Pranking with ranking, Advances in Neural Information Processing Systems 14, 2001. ,
Multivariate decoding of brain images using ordinal regression, NeuroImage, vol.81, pp.347-357, 2013. ,
Agnostic Learning of Monomials by Halfspaces Is Hard, SIAM Journal on Computing, vol.41, issue.6, pp.1558-1590, 2012. ,
DOI : 10.1137/120865094
Econometric analysis, 1997. ,
The numeric rating scale for clinical pain measurement: A ratio measure? Pain Practice, pp.310-316, 2003. ,
Prediction of Ordinal Classes Using Regression Trees, Fundamenta Informaticae, vol.47, issue.1, pp.1-13, 2001. ,
DOI : 10.1007/3-540-39963-1_45
Multicategory Support Vector Machines, Journal of the American Statistical Association, vol.99, issue.465, pp.9967-81, 2004. ,
DOI : 10.1198/016214504000000098
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.1879
Ordinal Regression by Extended Binary Classiication, Advances in Neural Information Processing Systems (NIPS), 2007. ,
Large-margin thresholded ensembles for ordinal regression: Theory and practice, Algorithmic Learning Theory, pp.319-333, 2006. ,
A note on margin-based loss functions in classification, Statistics & Probability Letters, vol.68, issue.1, pp.73-82, 2004. ,
DOI : 10.1016/j.spl.2004.03.002
Regression Models for Ordinal Data, Journal of the Royal Statistical Society, vol.42, issue.2, pp.109-142, 1980. ,
Cost-sensitive multi-class classiication from probability estimates, Proceedings of the 25th international conference on Machine learning, pp.712-719, 2008. ,
Partial Proportional Odds Models for Ordinal Response Variables, Applied Statistics, vol.39, issue.2, pp.205-217, 1990. ,
DOI : 10.2307/2347760
Classiication Calibration Dimension for General Multiclass Losses, Advances in Neural Information Processing Systems (NIPS), 2012. ,
Loss Functions for Preference Levels : Regression with Discrete Ordered Labels, Proceedings of the IJCAI Multidisciplinary Workshop on Advances in Preference Handling, 2005. ,
On the maximal monotonicity of subdifferential mappings, Pacific Journal of Mathematics, vol.33, issue.1, pp.209-216, 1970. ,
DOI : 10.2140/pjm.1970.33.209
Ranking with large margin principle : Two approaches, Advances in Neural Information Processing Systems (NIPS), 2003. ,
Support Vector Machines are Universally Consistent, Journal of Complexity, vol.18, issue.3, pp.768-791, 2002. ,
DOI : 10.1006/jcom.2002.0642
URL : http://doi.org/10.1006/jcom.2002.0642
Consistent nonparametric regression. The Annals of Statistics, pp.595-620, 1977. ,
On the Consistency of Multiclass Classiication Methods, Journal of Machine Learning Research, vol.8, pp.1007-1025, 2007. ,
Statistical Behavior and Consistency of Classiication Methods based on Convex Risk Minimization. The Annals of Statistics, pp.56-85, 2004. ,
A perceptual-to-conceptual gradient of word coding along the ventral path, 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014. ,
DOI : 10.1109/PRNI.2014.6858512
URL : https://hal.archives-ouvertes.fr/hal-00986606
Data-driven HRF estimation for encoding and decoding models, NeuroImage, vol.104, pp.209-220, 2015. ,
DOI : 10.1016/j.neuroimage.2014.09.060
URL : https://hal.archives-ouvertes.fr/hal-00952554
HRF Estimation Improves Sensitivity of fMRI Encoding and Decoding Models, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013. ,
DOI : 10.1109/PRNI.2013.50
URL : https://hal.archives-ouvertes.fr/hal-00821946
Learning to Rank from Medical Imaging Data, Proceedings of the 3rd International Workshop on Machine Learning in Medical Imaging, 2012. ,
DOI : 10.1007/978-3-642-35428-1_29
URL : https://hal.archives-ouvertes.fr/hal-00717990
Improved Brain Pattern Recovery through Ranking Approaches, 2012 Second International Workshop on Pattern Recognition in NeuroImaging, 2012. ,
DOI : 10.1109/PRNI.2012.23
URL : https://hal.archives-ouvertes.fr/hal-00717954
Decoding perceptual thresholds from MEG/EEG, 2014 International Workshop on Pattern Recognition in Neuroimaging, 2014. ,
DOI : 10.1109/PRNI.2014.6858510
URL : https://hal.archives-ouvertes.fr/hal-01032909
On the Consistency of Ordinal Regression Methods ,
URL : https://hal.archives-ouvertes.fr/hal-01054942
API design for machine learning software: experiences from the scikit-learn project, European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00856511
Second Order Scattering Descriptors Predict fMRI Activity Due to Visual Textures, 2013 International Workshop on Pattern Recognition in Neuroimaging, 2013. ,
DOI : 10.1109/PRNI.2013.11
URL : https://hal.archives-ouvertes.fr/hal-00834928
Machine learning for neuroimaging with scikit-learn, Frontiers in Neuroinformatics, vol.8, 2014. ,
DOI : 10.3389/fninf.2014.00014
URL : https://hal.archives-ouvertes.fr/hal-01093971
Automatic pathology classiication using a single feature machine learning support-vector machines, SPIE Medical Imaging International Society for Optics and Photonics, pp.903524-903524, 2014. ,
Hemodynamic Estimation Based on Consensus Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.211-215, 2013. ,
DOI : 10.1109/PRNI.2013.61
URL : https://hal.archives-ouvertes.fr/hal-00854621
Least squares solutions of bilinear equations, Systems & Control Letters, vol.55, issue.6, pp.466-472, 2006. ,
DOI : 10.1016/j.sysconle.2005.09.010
Hemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.180-188, 2012. ,
DOI : 10.1007/978-3-642-33454-2_23
URL : https://hal.archives-ouvertes.fr/hal-00859388
A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies, NeuroImage, vol.98, pp.61-72, 2014. ,
DOI : 10.1016/j.neuroimage.2014.04.052
Regression Models for Ordinal Data : A Machine Learning Approach, 1999. ,
Bayesian deconvolution fMRI data using bilinear dynamical systems, NeuroImage, vol.42, issue.4, pp.1381-96, 2008. ,
DOI : 10.1016/j.neuroimage.2008.05.052
Regression Models for Ordinal Data, Journal of the Royal Statistical Society, vol.42, issue.2, pp.109-142, 1980. ,
Large scale learning to rank, NIPS 2009 Workshop on Advances in Ranking, pp.1-6, 2009. ,
Spatially Adaptive Mixture Modeling for Analysis of fMRI Time Series, IEEE Transactions on Medical Imaging, vol.29, issue.4, pp.1059-1074, 2010. ,
DOI : 10.1109/TMI.2010.2042064
URL : https://hal.archives-ouvertes.fr/cea-00470594
Multiscale adaptive smoothing models for the hemodynamic response function in fMRI, The Annals of Applied Statistics, vol.7, issue.2, pp.904-935, 2013. ,
DOI : 10.1214/12-AOAS609SUPP