H. Abdi, L. J. Williams, A. C. Connolly, M. I. Gobbini, J. P. Dunlop et al., Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization, Computational and Mathematical Methods in Medicine, vol.2, issue.8, pp.1-15, 2012.
DOI : 10.1162/jocn_a_00079
URL : http://doi.org/10.1155/2012/634165

M. Aizerman, E. Braverman, and L. Rozonoer, Theoretical foundations of the potential function method in pattern recognition learning, In: Automation and Remote Control, vol.25, pp.821-837, 1964.

P. Arbeláez, B. Hariharan, C. Gu, S. Gupta, L. Bourdev et al., Semantic segmentation using regions and parts, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3378-3385, 2012.
DOI : 10.1109/CVPR.2012.6248077

J. Ashburner, Computational anatomy with the SPM software, Magnetic Resonance Imaging, vol.27, issue.8, pp.1163-1174, 2009.
DOI : 10.1016/j.mri.2009.01.006

J. Ashburner, SPM: A history, NeuroImage, vol.62, issue.2, pp.791-800, 2012.
DOI : 10.1016/j.neuroimage.2011.10.025
URL : http://doi.org/10.1016/j.neuroimage.2011.10.025

G. Auzias, C. Breuil, S. Takerkart, and C. Deruelle, Detectability of brain structure abnormalities related to autism through MRI-derived measures from multiple scanners, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp.314-317, 2014.
DOI : 10.1109/BHI.2014.6864366

G. Auzias, L. Brun, C. Deruelle, and O. Coulon, Deep sulcal landmarks: Algorithmic and conceptual improvements in the definition and extraction of sulcal pits, NeuroImage, vol.111, issue.109, pp.12-25, 2015.
DOI : 10.1016/j.neuroimage.2015.02.008

G. Auzias, J. Lefevre, A. L. Troter, C. Fischer, M. Perrot et al., Model-Driven Harmonic Parameterization of the Cortical Surface: HIP-HOP, IEEE Transactions on Medical Imaging, vol.32, issue.5, pp.873-887, 2013.
DOI : 10.1109/TMI.2013.2241651

G. Auzias, M. Viellard, S. Takerkart, N. Villeneuve, F. Poinso et al., Atypical sulcal anatomy in young children with autism spectrum disorder, NeuroImage: Clinical, vol.4, issue.76, pp.593-603, 2014.
DOI : 10.1016/j.nicl.2014.03.008

C. I. Baker, E. Peli, N. Knouf, and N. G. Kanwisher, Reorganization of Visual Processing in Macular Degeneration, Journal of Neuroscience, vol.25, issue.3, pp.614-618, 2005.
DOI : 10.1523/JNEUROSCI.3476-04.2005

A. Barachant, S. Bonnet, M. Congedo, and C. Jutten, Classification of covariance matrices using a Riemannian-based kernel for BCI applications, Neurocomputing, vol.112, issue.127, pp.172-178, 2013.
DOI : 10.1016/j.neucom.2012.12.039
URL : https://hal.archives-ouvertes.fr/hal-00820475

T. E. Behrens, H. Johansen-berg, M. W. Woolrich, S. M. Smith, C. A. Wheeler-kingshott et al., Noninvasive mapping of connections between human thalamus and cortex using diffusion imaging, Nat Neurosci, vol.67, pp.750-757, 2003.

O. Beijbom, Domain adaptations for computer vision applications, 2012.

S. Ben-david, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira et al., A theory of learning from different domains, Machine Learning, vol.60, issue.1-2, pp.151-175, 2010.
DOI : 10.1007/s10994-009-5152-4

Y. Bengio, Deep learning of representations for unsupervised and transfer learning, Unsupervised and Transfer Learning Challenges in Machine Learning, p.19, 2012.

M. Bilello, M. Arkuszewski, P. Nucifora, I. Nasrallah, E. R. Melhem et al., Multiple Sclerosis: Identification of Temporal Changes in Brain Lesions with Computer-Assisted Detection Software, The neuroradiology journal 26, pp.143-150, 2013.
DOI : 10.1148/radiol.2471070816

T. Blumensath, S. Jbabdi, M. F. Glasser, D. C. Essen, K. Ugurbil et al., Spatially constrained hierarchical parcellation of the brain with resting-state fMRI, NeuroImage, vol.76, pp.313-324, 2013.
DOI : 10.1016/j.neuroimage.2013.03.024

K. M. Borgwardt and H. Kriegel, Shortest-Path Kernels on Graphs, Fifth IEEE International Conference on Data Mining (ICDM'05), pp.74-81, 2005.
DOI : 10.1109/ICDM.2005.132

M. Boucher, S. Whitesides, and A. Evans, Depth potential function for folding pattern representation, registration and analysis, Medical Image Analysis, vol.13, issue.2, pp.203-217, 2009.
DOI : 10.1016/j.media.2008.09.001

M. Brett, J. Taylor, C. Burns, K. J. Millman, F. Perez et al., NIPY: an open library and development framework for FMRI data analysis, NeuroImage, vol.47, pp.47-196, 2009.
DOI : 10.1016/S1053-8119(09)72223-2

J. Bruna and S. Mallat, Invariant Scattering Convolution Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1872-1886, 2013.
DOI : 10.1109/TPAMI.2012.230
URL : http://arxiv.org/abs/1203.1513

L. Bruzzone and M. Marconcini, Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.770-787, 2010.
DOI : 10.1109/TPAMI.2009.57

E. T. Bullmore, J. Suckling, S. Overmeyer, S. Rabe-hesketh, E. Taylor et al., Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain, IEEE Transactions on Medical Imaging, vol.18, issue.1, pp.32-42, 1999.
DOI : 10.1109/42.750253

H. Bunke, On a relation between graph edit distance and maximum common subgraph, Pattern Recognition Letters, vol.18, issue.8, pp.689-694, 1997.
DOI : 10.1016/S0167-8655(97)00060-3

C. Cabral, M. Silveira, and P. Figueiredo, Decoding visual brain states from fMRI using an ensemble of classifiers, Pattern Recognition, vol.45, issue.6, pp.2064-2074, 2012.
DOI : 10.1016/j.patcog.2011.04.015

Y. Cao, J. Xu, T. Y. Liu, H. Li, Y. Huang et al., Adapting ranking SVM to document retrieval, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, pp.186-193, 2006.
DOI : 10.1145/1148170.1148205
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9218

O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning Adaptive computation and machine learning, 2006.

R. Chattopadhyay, Q. Sun, W. Fan, I. Davidson, S. Panchanathan et al., Multisource domain adaptation and its application to early detection of fatigue, ACM Transactions on Knowledge Discovery from Data, vol.6, issue.4, pp.18-127, 2012.
DOI : 10.1145/2382577.2382582

M. Chen, K. Q. Weinberger, J. Blitzer, J. Shawe-taylor, R. S. Zemel et al., Co-Training for Domain Adaptation Advances in Neural Information Processing Systems 24, pp.2456-2464, 2011.

Y. Chen, P. Namburi, L. T. Elliott, J. Heinzle, C. S. Soon et al., Cortical surface-based searchlight decoding, NeuroImage, vol.56, issue.2, pp.582-592, 2011.
DOI : 10.1016/j.neuroimage.2010.07.035

D. Ciresan, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3642-3649, 2012.
DOI : 10.1109/CVPR.2012.6248110

J. A. Clithero, D. V. Smith, R. M. Carter, and S. A. , Withinand cross-participant classifiers reveal different neural coding of information, NeuroImage, vol.562, pp.699-708, 2011.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.1-25, 1995.
DOI : 10.1007/BF00994018

O. Coulon, J. Mangin, J. Poline, M. Zilbovicius, D. Roumenov et al., Structural Group Analysis of Functional Activation Maps, NeuroImage, vol.11, issue.6, pp.767-782, 2000.
DOI : 10.1006/nimg.2000.0580

M. N. Coutanche, S. L. Thompson-schill, and R. T. Schultz, Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity, NeuroImage, vol.57, issue.1, pp.113-123, 2011.
DOI : 10.1016/j.neuroimage.2011.04.016

K. Crammer, M. Kearns, and J. Wortman, Learning from multiple sources, In: The Journal of Machine Learning Research, vol.9, pp.1757-1774, 2008.

R. Cuingnet, J. A. Glaunes, M. Chupin, H. Benali, and O. Colliot, Spatial and Anatomical Regularization of SVM: A General Framework for Neuroimaging Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.3, pp.682-696, 2013.
DOI : 10.1109/TPAMI.2012.142
URL : https://hal.archives-ouvertes.fr/hal-00790079

G. Dahl, D. Yu, L. Deng, and A. Acero, Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition, Audio , Speech, and Language Processing, pp.30-42, 2012.
DOI : 10.1109/TASL.2011.2134090
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.227.8990

W. Dai, G. Xue, Q. Yang, and Y. Yu, Transferring naive bayes classifiers for text classification, PROCEEDINGS OF THE NATIONAL CON- FERENCE ON ARTIFICIAL INTELLIGENCE, vol.22, p.540, 1999.

A. Dale, Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction, NeuroImage 9, pp.179-194, 1999.

H. Daumé, I. , and D. Marcu, Domain Adaptation for Statistical Classifiers, Journal of Artificial Intelligence Research (JAIR), vol.26, pp.101-126, 2006.

C. Davatzikos, K. Ruparel, Y. Fan, D. G. Shen, M. Acharyya et al., Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection, NeuroImage, vol.28, issue.3, pp.663-668, 2005.
DOI : 10.1016/j.neuroimage.2005.08.009

C. Destrieux, B. Fischl, A. Dale, and E. Halgren, Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature, NeuroImage, vol.53, issue.1, pp.1-15, 2010.
DOI : 10.1016/j.neuroimage.2010.06.010

T. G. Dietterich, Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, Neural Computation, vol.6, issue.7, pp.1895-1923, 1998.
DOI : 10.1007/BF00058655
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.2536

L. Duan, I. W. Tsang, D. Xu, and T. Chua, Domain adaptation from multiple sources via auxiliary classifiers, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.289-296, 2009.
DOI : 10.1145/1553374.1553411
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5107

E. Duchesnay, A. Cachia, A. Roche, D. Rivière, Y. Cointepas et al., Classification Based on Cortical Folding Patterns, IEEE Transactions on Medical Imaging, vol.26, issue.4, pp.553-565, 2007.
DOI : 10.1109/TMI.2007.892501

M. Eshera and K. Fu, An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, issue.5, pp.604-618, 1986.
DOI : 10.1109/TPAMI.1986.4767835

D. C. Essen and D. L. Dierker, Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex, Neuron, vol.56, issue.2, pp.209-225, 2007.
DOI : 10.1016/j.neuron.2007.10.015

J. A. Etzel, J. M. Zacks, and T. S. Braver, Searchlight analysis: Promise, pitfalls, and potential, NeuroImage, vol.78, pp.261-269, 2013.
DOI : 10.1016/j.neuroimage.2013.03.041
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3988828

M. Fang, Y. Guo, X. Zhang, and X. Li, Multi-source transfer learning based on label shared subspace, Pattern Recognition Letters, vol.51, pp.101-106, 2015.
DOI : 10.1016/j.patrec.2014.08.011

P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010.
DOI : 10.1109/TPAMI.2009.167

A. Feragen, N. Kasenburg, J. Petersen, M. De-bruijne, and K. Borgwardt, Scalable kernels for graphs with continuous attributes Advances in Neural Information Processing Systems 26, pp.216-224, 2013.

C. Ferri, J. Hernández-orallo, and R. Modroiu, An experimental comparison of performance measures for classification, Pattern Recognition Letters, vol.30, issue.1, pp.27-38, 2009.
DOI : 10.1016/j.patrec.2008.08.010

B. Fischl, M. I. Sereno, R. B. Tootell, and A. M. Dale, High-resolution intersubject averaging and a coordinate system for the cortical surface, Human Brain Mapping, vol.179, issue.4, pp.272-284, 1999.
DOI : 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4

G. Flandin, F. Kherif, X. Pennec, G. Malandain, N. Ayache et al., Improved detection sensitivity of functional MRI data using a brain parcellation technique, Proc. 5th MICCAI. LNCS 2488 (Part I). INRIA Projet Epidaure, pp.467-474, 2002.

E. Formisano, D. Kim, F. Di-salle, P. Van-de-moortele, K. Ugurbil et al., Mirror-Symmetric Tonotopic Maps in Human Primary Auditory Cortex, Neuron, vol.40, issue.4, pp.859-869, 2003.
DOI : 10.1016/S0896-6273(03)00669-X

P. Frasconi, M. Gori, and A. Sperduti, On the efficient classification of data structures by neural networks, Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI 97, 1997.

G. B. Frisoni, N. C. Fox, C. R. Jack, P. Scheltens, and P. M. Thompson, The clinical use of structural MRI in Alzheimer disease, Nature Reviews Neurology, vol.15, issue.2, pp.67-77, 2010.
DOI : 10.1038/nrneurol.2009.215

K. J. Friston, Statistical parametric mapping: the analysis of funtional brain images, pp.43-55, 2007.

É. Fromont, M. Cordier, and R. Quiniou, Learning from Multi-source Data, Knowledge Discovery in Databases: PKDD 2004, pp.503-505, 2004.
DOI : 10.1007/978-3-540-30116-5_47
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.407.6486

T. Gärtner, P. A. Flach, and S. Wrobel, On Graph Kernels: Hardness Results and Efficient Alternatives, Proc. of the 16th Conf. on Computational Learning Theory, p.79, 2003.
DOI : 10.1007/978-3-540-45167-9_11

K. Geras and C. Sutton, Multiple-source cross-validation, Proceedings of the 30th International Conference on Machine Learning (ICML-13). 2013, pp.1292-1300

K. Gkirtzou, J. Honorio, D. Samaras, R. Goldstein, and M. B. Blaschko, fMRI Analysis with Sparse Weisfeiler-Lehman Graph Statistics, Machine Learning in Medical Imaging, pp.90-97, 2013.
DOI : 10.1007/978-3-319-02267-3_12
URL : https://hal.archives-ouvertes.fr/hal-00845068

L. Goldfarb, D. Gay, O. Golubitsky, and D. Korkin, What is a Structural Representation? (Second Version), 2004.

C. D. Good, I. Johnsrude, J. Ashburner, R. N. Henson, K. J. Friston et al., Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains, NeuroImage, vol.14, issue.3, pp.685-700, 2001.
DOI : 10.1006/nimg.2001.0857

A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt et al., Covariate Shift by Kernel Mean Matching, Dataset shift in machine learning 3, pp.115-116, 2009.
DOI : 10.7551/mitpress/9780262170055.003.0008
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.8929

D. N. Greve, L. Van-der-haegen, Q. Cai, S. Stufflebeam, M. R. Sabuncu et al., A Surface-based Analysis of Language Lateralization and Cortical Asymmetry, Journal of Cognitive Neuroscience, vol.211, issue.9, pp.1477-1492, 2013.
DOI : 10.1016/j.neuroimage.2010.01.101

L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor, Interpretable whole-brain prediction analysis with GraphNet, NeuroImage, vol.72, pp.304-321, 2013.
DOI : 10.1016/j.neuroimage.2012.12.062

S. J. Hanson and A. Schmidt, High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories, NeuroImage, vol.54, issue.2, pp.1715-1734, 2011.
DOI : 10.1016/j.neuroimage.2010.08.028

D. Haussler, Convolution kernels on discrete structures. UCSC-CRL-99-10, pp.51-79, 1999.

J. V. Haxby, M. I. Gobbini, M. L. Furey, A. Ishai, J. L. Schouten et al., Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex, Science, vol.293, issue.5539, pp.2425-2430, 2001.
DOI : 10.1126/science.1063736

J. V. Haxby, J. S. Guntupalli, A. C. Connolly, Y. O. Halchenko, B. R. Conroy et al., A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex, Neuron, vol.72, issue.2, pp.404-416, 2011.
DOI : 10.1016/j.neuron.2011.08.026

J. Haynes and G. Rees, Decoding mental states from brain activity in humans, Nature Reviews Neuroscience, vol.16, issue.7, pp.523-534, 2006.
DOI : 10.1038/nrn1931

G. E. Hinton and R. R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-507, 2006.
DOI : 10.1126/science.1127647

J. Honorio, D. Tomasi, R. Z. Goldstein, H. Leung, and D. Samaras, Can a Single Brain Region Predict a Disorder?, IEEE Transactions on Medical Imaging, vol.31, issue.11, pp.2062-2072, 2012.
DOI : 10.1109/TMI.2012.2206047

P. Huang, G. Wang, and S. Qin, Boosting for transfer learning from multiple data sources, Pattern Recognition Letters, vol.33, issue.5, pp.568-579, 2012.
DOI : 10.1016/j.patrec.2011.11.023

C. Humphries, E. Liebenthal, and J. R. Binder, Tonotopic organization of human auditory cortex, NeuroImage, vol.50, issue.3, pp.1202-1211, 2010.
DOI : 10.1016/j.neuroimage.2010.01.046

K. Im, H. J. Jo, J. Mangin, A. C. Evans, S. I. Kim et al., Spatial Distribution of Deep Sulcal Landmarks and Hemispherical Asymmetry on the Cortical Surface, Cerebral Cortex, vol.20, issue.3, pp.602-613, 2010.
DOI : 10.1093/cercor/bhp127

K. Im, R. Pienaar, J. Lee, J. Seong, Y. Y. Choi et al., Quantitative comparison and analysis of sulcal patterns using sulcal graph matching: A twin study, NeuroImage, vol.57, issue.3, pp.1077-86, 2011.
DOI : 10.1016/j.neuroimage.2011.04.062

K. Im, R. Pienaar, M. J. Paldino, N. Gaab, A. M. Galaburda et al., Quantification and Discrimination of Abnormal Sulcal Patterns in Polymicrogyria, Cerebral Cortex, vol.23, issue.12, pp.3007-3022, 2012.
DOI : 10.1093/cercor/bhs292

K. Im, N. M. Raschle, S. A. Smith, N. Grant, and . Gaab, Atypical Sulcal Pattern in Children with Developmental Dyslexia and At-Risk Kindergarteners, Cerebral Cortex, vol.26, issue.3, p.77, 2015.
DOI : 10.1093/cercor/bhu305

S. Janson, Large deviations for sums of partly dependent random variables, Random Structures and Algorithms, vol.10, issue.3, pp.234-248, 2004.
DOI : 10.1002/rsa.20008

B. Jie, D. Zhang, C. Wee, and D. Shen, Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification, Human Brain Mapping, vol.49, issue.7, pp.2876-97, 2014.
DOI : 10.1371/journal.pone.0037828
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4116356

T. Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.133-142, 2002.
DOI : 10.1145/775047.775067
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.3161

T. Joachims, Transductive Learning via Spectral Graph Partitioning, International Conference on Machine Learning (ICML). 2003, pp.290-297

Y. Kamitani and F. Tong, 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

D. R. Kisku, A. Rattani, E. Grosso, and M. Tistarelli, Face Identification by SIFT-based Complete Graph Topology, 2007 IEEE Workshop on Automatic Identification Advanced Technologies, pp.63-68, 2007.
DOI : 10.1109/AUTOID.2007.380594

A. Knops, B. Thirion, E. M. Hubbard, V. Michel, and S. Dehaene, Recruitment of an Area Involved in Eye Movements During Mental Arithmetic, Science, vol.324, issue.5934, pp.1583-1585, 2009.
DOI : 10.1126/science.1171599
URL : https://hal.archives-ouvertes.fr/hal-00504101

R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, pp.1137-1143, 1995.

S. Koyamada, Y. Shikauchi, K. Nakae, M. Koyama, and S. Ishii, Deep learning of fMRI big data: a novel approach to subject-transfer decoding, 2015.

N. Kriege and P. Mutzel, Subgraph Matching Kernels for Attributed GraphsICML-12), Proceedings of the 29th International Conference on Machine Learning, pp.1015-1022, 2012.

N. Kriegeskorte, R. Goebel, and P. Bandettini, Information-based functional brain mapping, Proceedings of the National Academy of Sciences, vol.103, issue.10, pp.3863-3868, 2006.
DOI : 10.1073/pnas.0600244103
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1383651

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks " . In: Advances in neural information processing systems, pp.1097-1105, 2012.

S. Laconte, S. Strother, V. Cherkassky, J. Anderson, and X. Hu, 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

J. Lafferty, A. Mccallum, and F. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Proc. 18th Int. Conf. on Machine Learning, pp.282-289, 2001.

D. Lashkari, R. Sridharan, E. Vul, P. Hsieh, N. Kanwisher et al., Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data, NeuroImage, vol.59, issue.2, pp.1348-1368, 2012.
DOI : 10.1016/j.neuroimage.2011.08.031

J. Lefevre, F. Leroy, S. Khan, J. Dubois, P. S. Huppi et al., Identification of Growth Seeds in the Neonate Brain through Surfacic Helmholtz Decomposition, Information Processing in Medical Imaging, 21st International Conference, pp.252-263, 2009.
DOI : 10.1007/978-3-540-75759-7_16

Q. Li, Literature Survey: Domain Adaptation Algorithms for Natural Language Processing, 2012.

Z. Lin, G. Ding, and M. Hu, Multi-source image auto-annotation, 2013 IEEE International Conference on Image Processing, pp.2567-2571, 2013.
DOI : 10.1109/ICIP.2013.6738529

T. Lindeberg, Effective scale: a natural unit for measuring scale-space lifetime " . In: Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.1510, issue.87 111, pp.1068-1074, 1993.

T. Y. Liu, Learning to Rank for Information Retrieval, Foundations and Trends?? in Information Retrieval, vol.3, issue.3, pp.225-331, 2009.
DOI : 10.1561/1500000016

A. Lorbert and P. J. Ramadge, Kernel Hyperalignment, pp.1799-1807, 2012.

D. G. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.4065

B. Lv, J. Li, H. He, M. Li, M. Zhao et al., Gender consistency and difference in healthy adults revealed by cortical thickness, NeuroImage, vol.53, issue.2, pp.373-382, 2010.
DOI : 10.1016/j.neuroimage.2010.05.020

J. Maclaren, Z. Han, S. B. Vos, N. Fischbein, and R. Bammer, Reliability of brain volume measurements: A test-retest dataset, Scientific Data, vol.31, p.140037, 2014.
DOI : 10.1038/sdata.2014.37

P. Mahé, L. Ralaivola, V. Stoven, and J. Vert, The Pharmacophore Kernel for Virtual Screening with Support Vector Machines, Journal of Chemical Information and Modeling, vol.46, issue.5, pp.2003-2014, 2006.
DOI : 10.1021/ci060138m

P. Mahé and J. P. Vert, Graph kernels based on tree patterns for molecules, Machine Learning, vol.21, issue.Suppl.??1, pp.3-35, 2009.
DOI : 10.1007/s10994-008-5086-2

A. Mahmoudi, S. Takerkart, F. Regragui, D. Boussaoud, and A. Brovelli, Multivoxel Pattern Analysis for fMRI Data: A Review, Computational and Mathematical Methods in Medicine, vol.2011, issue.1, pp.1-14, 2012.
DOI : 10.1006/nimg.1999.0435

Y. Mansour, M. Mohri, and A. Rostamizadeh, Domain adaptation with multiple sources Advances in neural information processing systems, pp.1041-1048, 2009.

A. Margolis, A literature review of domain adaptation with unlabeled data, p.35, 2011.

A. F. Marquand, M. Brammer, S. C. Williams, and O. M. Doyle, Bayesian multi-task learning for decoding multi-subject neuroimaging data, NeuroImage, vol.92, pp.298-311, 2014.
DOI : 10.1016/j.neuroimage.2014.02.008

D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, 1982.
DOI : 10.7551/mitpress/9780262514620.001.0001

J. Menke and T. R. Martinez, Using permutations instead of student's t distribution for p-values in paired-difference algorithm comparisons, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004.
DOI : 10.1109/IJCNN.2004.1380138

K. Meyer, J. T. Kaplan, R. Essex, C. Webber, H. Damasio et al., Predicting visual stimuli on the basis of activity in auditory cortices, Nature Neuroscience, vol.276, issue.6, pp.667-668, 2010.
DOI : 10.1038/nn.2533

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin et al., A supervised clustering approach for fMRI-based inference of brain states, Pattern Recognition, vol.45, issue.6, pp.2041-2049, 2012.
DOI : 10.1016/j.patcog.2011.04.006
URL : https://hal.archives-ouvertes.fr/inria-00589201

V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion, 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

T. Mikolov, A. Deoras, S. Kombrink, L. Burget, and J. Cernocký, Empirical Evaluation and Combination of Advanced Language Modeling Techniques, Proceedings of Interspeech 2011 2011. = {Tomá? Mikolov and Anoop Deoras and Stefan Kombrink and Luká? Burget and Ja? Cernocký}. Florence, IT: International Speech Communication Association, pp.605-608, 2011.

M. Misaki, Y. Kim, P. A. Bandettini, and N. Kriegeskorte, Comparison of multivariate classifiers and response normalizations for pattern-information fMRI, NeuroImage, vol.53, issue.1, pp.103-118, 2010.
DOI : 10.1016/j.neuroimage.2010.05.051

T. Mitchell, R. Hutchinson, R. Niculescu, F. Pereir-a, X. Wang et al., Learning to Decode Cognitive States from Brain Images, Machine Learning, vol.57, issue.1/2, pp.145-175, 2004.
DOI : 10.1023/B:MACH.0000035475.85309.1b

M. Moerel, F. D. Martino, and E. Formisano, Processing of Natural Sounds in Human Auditory Cortex: Tonotopy, Spectral Tuning, and Relation to Voice Sensitivity, Journal of Neuroscience, vol.32, issue.41, pp.41-14205, 2012.
DOI : 10.1523/JNEUROSCI.1388-12.2012

F. Mokhtari and G. Hossein-zadeh, Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks, Journal of Neuroscience Methods, vol.212, issue.2, pp.259-268, 2013.
DOI : 10.1016/j.jneumeth.2012.10.012

A. Morel, P. E. Garraghty, and J. H. Kaas, Tonotopic organization, architectonic fields, and connections of auditory cortex in macaque monkeys, The Journal of Comparative Neurology, vol.171, issue.3, pp.437-459, 1993.
DOI : 10.1002/cne.903350312

J. Mourão-miranda, A. L. Bokde, C. Born, H. Hampel, and M. Stetter, Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data, NeuroImage, vol.28, issue.4, pp.980-995, 2005.
DOI : 10.1016/j.neuroimage.2005.06.070

S. G. Mueller, M. W. Weiner, L. J. Thal, R. C. Petersen, C. R. Jack et al., Ways toward an early diagnosis in Alzheimer???s disease: The Alzheimer???s Disease Neuroimaging Initiative (ADNI), Alzheimer's & Dementia, vol.1, issue.1, pp.55-66, 2005.
DOI : 10.1016/j.jalz.2005.06.003

J. A. Mumford, B. O. Turner, F. G. Ashby, and R. A. Poldrack, Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses, NeuroImage, vol.59, issue.3, pp.2636-2643, 2012.
DOI : 10.1016/j.neuroimage.2011.08.076

T. E. Nichols and A. P. Holmes, Nonparametric permutation tests for functional neuroimaging: A primer with examples, Human Brain Mapping, vol.4, issue.1, pp.1-25, 2002.
DOI : 10.1002/hbm.1058

H. Niederreiter and S. I. , Integration of nonperiodic functions of two variables by Fibonacci lattice rules, Journal of Computational and Applied Mathematics, vol.51, issue.1, pp.57-70, 1994.
DOI : 10.1016/0377-0427(92)00004-S

K. A. Norman, S. M. Polyn, G. J. Detre, and J. V. Haxby, Beyond mind-reading: multi-voxel pattern analysis of fMRI data, Trends in Cognitive Sciences, vol.10, issue.9, pp.424-430, 2006.
DOI : 10.1016/j.tics.2006.07.005

S. J. Pan, I. Tsang, J. Kwok, and Q. Yang, Domain Adaptation via Transfer Component Analysis, Neural Networks, IEEE Transactions on 22, pp.199-210, 2011.
DOI : 10.1109/TNN.2010.2091281
URL : http://c2inet.sce.ntu.edu.sg/ivor/publication/tca_tnn.pdf

S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191

T. Pavlidis, Structural pattern recognition, xii, 302 p. : (cit, pp.49-79, 1977.
DOI : 10.1007/978-3-642-88304-0

F. Pereira, T. Mitchell, and M. Botvinick, Machine learning classifiers and fMRI: A tutorial overview, NeuroImage 45, pp.199-209, 2009.
DOI : 10.1016/j.neuroimage.2008.11.007

J. M. Phillips and S. Venkatasubramanian, A gentle introduction to the kernel distance, 2011.

J. Piater, P. Cohen, X. Zhang, and M. Atighetchi, A Randomized ANOVA Procedure For Comparing Performance Curves, Proc. Fifteenth International Conference on Machine Learning, pp.430-438, 1998.

S. Poline and . Dehaene, Fast reproducible identification and large-scale databasing of individual functional cognitive networks, BMC Neuroscience, vol.81, p.91, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00784462

R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson, Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals, Psychological Science, vol.28, issue.11, pp.1364-1372, 2009.
DOI : 10.1111/j.1467-9280.2009.02460.x

J. Poline and B. M. Mazoyer, Analysis of Individual Positron Emission Tomography Activation Maps by Detection of High Signal-to-Noise-Ratio Pixel Clusters, J Cereb Blood Flow Metab, vol.133, issue.87, pp.425-437, 1993.

S. Polyn, G. Detre, S. Takerkart, V. Natu, M. Benharrosh et al., A Matlab-based toolbox to facilitate multi-voxel pattern classification of fMRI data, Proceedings of HBM. HBM, 2005.

N. Przulj, D. G. Corneil, and I. Jurisica, Modeling interactome: scale-free or geometric?, Bioinformatics, vol.20, issue.18, pp.3508-3515, 2004.
DOI : 10.1093/bioinformatics/bth436
URL : http://arxiv.org/abs/q-bio/0404017

L. Ralaivola, S. J. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Networks, special issue on Neural Networks and Kernel Methods for Structured Domain 18, pp.1093-1110, 2005.
DOI : 10.1016/j.neunet.2005.07.009

L. Ralaivola, M. Szafranski, and G. Stempfel, Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary beta-Mixing Processes, Journal of Machine Learning Research, vol.11, pp.1927-1956, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00415162

J. Ramon and T. Gärtner, Expressivity versus efficiency of graph kernels, Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences, pp.65-74, 2003.

P. M. Rasmussen, K. H. Madsen, T. E. Lund, and L. K. Hansen, Visualization of nonlinear kernel models in neuroimaging by sensitivity maps, NeuroImage, vol.55, issue.3, pp.1120-1131, 2011.
DOI : 10.1016/j.neuroimage.2010.12.035

J. Régis, J. Mangin, T. Ochiai, V. Frouin, D. Rivière et al., Sulcal root generic model: a hypothesis to overcome the variability of the human cortex folding patterns In: Neurologia medico-chirurgica 45, pp.1-17, 2005.

J. Richiardi, S. Achard, H. Bunke, and D. Van-de-ville, Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience, IEEE Signal Processing Magazine, vol.30, issue.3, pp.58-70, 2013.
DOI : 10.1109/MSP.2012.2233865

K. Riesen and H. Bunke, Graph Classification Based On Vector Space Embedding, International Journal of Pattern Recognition and Artificial Intelligence, vol.23, pp.6-1053, 2009.

A. N. Ruigrok, G. Salimi-khorshidi, M. Lai, S. Baron-cohen, M. V. Lombardo et al., A meta-analysis of sex differences in human brain structure, Neuroscience & Biobehavioral Reviews, vol.39, pp.34-50, 2014.
DOI : 10.1016/j.neubiorev.2013.12.004

R. M. Rustamov and L. Guibas, Hyperalignment of Multi-subject fMRI Data by Synchronized Projections, Proceedings of the 3rd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (MLINI). 2013 (cit, p.40
DOI : 10.1007/978-3-319-45174-9_12

S. Ryali, K. Supekar, D. A. Abrams, and V. M. On, Sparse logistic regression for whole-brain classification of fMRI data, NeuroImage, vol.51, issue.2, pp.752-764, 2010.
DOI : 10.1016/j.neuroimage.2010.02.040

M. R. Sabuncu and E. , Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study, Neuroinformatics, vol.47, issue.1, pp.31-46, 2015.
DOI : 10.1007/s12021-014-9238-1

M. R. Sabuncu, B. D. Singer, B. Conroy, R. E. Bryan, P. J. Ramadge et al., Function-based Intersubject Alignment of Human Cortical Anatomy, Cerebral Cortex, vol.20, issue.1, pp.130-140, 2010.
DOI : 10.1093/cercor/bhp085

G. Sanromà, R. Alquézar, and F. Serratosa, Attributed Graph Matching for Image-Features Association Using SIFT Descriptors, Structural, Syntactic , and Statistical Pattern Recognition, pp.254-263, 2010.
DOI : 10.1007/978-3-642-14980-1_24

S. Satpal and S. Sarawagi, Domain Adaptation of Conditional Probability Models Via Feature Subsetting, Knowledge Discovery in Databases: PKDD 2007, pp.224-235, 2007.
DOI : 10.1007/978-3-540-74976-9_23

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines , Regularization, Optimization, and Beyond, p.112, 2001.

J. M. Schott, N. C. Fox, C. Frost, R. I. Scahill, J. C. Janssen et al., Assessing the onset of structural change in familial Alzheimer's disease, Annals of Neurology, vol.55, issue.2, pp.181-188, 2003.
DOI : 10.1002/ana.10424

G. Schweikert, G. Rätsch, C. Widmer, and B. Schölkopf, An empirical analysis of domain adaptation algorithms for genomic sequence analysis, Advances in Neural Information Processing Systems, pp.1433-1440, 2009.

N. Shervashidze, P. Schweitzer, E. J. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, Weisfeiler-Lehman Graph Kernels, Journal of Machine Learning Research, vol.12, pp.2539-2561, 2011.

X. Shi, J. Paiement, D. Grangier, and S. Y. Philip, Learning from Heterogeneous Sources via Gradient Boosting Consensus, In: SDM. SIAM, pp.224-235, 2012.
DOI : 10.1137/1.9781611972825.20
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.221.5737

H. Shimodaira, Improving predictive inference under covariate shift by weighting the log-likelihood function, Journal of Statistical Planning and Inference, vol.90, issue.2, pp.227-244, 2000.
DOI : 10.1016/S0378-3758(00)00115-4

S. V. Shinkareva, V. L. Malave, M. A. Just, and T. M. Mitchell, Exploring commonalities across participants in the neural representation of objects, Human Brain Mapping, vol.10, issue.Suppl 2, pp.1375-1383, 2012.
DOI : 10.1002/hbm.21296

S. V. Shinkareva, R. Mason, V. L. Malave, W. Wang, T. M. Mitchell et al., Using fMRI Brain Activation to Identify Cognitive States Associated with Perception of Tools and Dwellings, PLoS ONE, vol.17, issue.1, p.1394, 2008.
DOI : 10.1371/journal.pone.0001394.s002

A. Smola, A. Gretton, L. Song, and B. Schölkopf, A Hilbert Space Embedding for Distributions, Algorithmic Learning Theory, pp.13-31, 2007.
DOI : 10.1073/pnas.0601231103

O. Sporns, G. Tononi, and R. Kötter, The Human Connectome: A Structural Description of the Human Brain, PLoS Computational Biology, vol.2, issue.4, p.42, 2005.
DOI : 1539-2791(2004)002[0019:IDAESF]2.0.CO;2

J. Stelzer, Y. Chen, and R. Turner, Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control, NeuroImage, vol.65, issue.87, pp.69-82, 2013.
DOI : 10.1016/j.neuroimage.2012.09.063

S. Sugiyama and K. Müller, Input-dependent estimation of generalization error under covariate shift, Statistics & Decisions, vol.23, issue.4/2005, pp.249-279, 2005.
DOI : 10.1524/stnd.2005.23.4.249

Q. Sun, R. Chattopadhyay, S. Panchanathan, and J. Ye, A two-stage weighting framework for multi-source domain adaptation Advances in neural information processing systems, pp.505-513, 2011.

S. Sun, A survey of multi-view machine learning, Neural Computing and Applications, pp.7-8, 2013.
DOI : 10.1007/s00521-013-1362-6

S. Sun, H. Shi, and Y. Wu, A survey of multi-source domain adaptation, Information Fusion, vol.24, pp.84-92, 2015.
DOI : 10.1016/j.inffus.2014.12.003

Z. Y. Sun, S. Klöppel, D. Rivière, M. Perrot, R. S. Frackowiak et al., The effect of handedness on the shape of the central sulcus, NeuroImage, vol.60, issue.1, pp.332-341, 2012.
DOI : 10.1016/j.neuroimage.2011.12.050

Z. Y. Sun, S. Klöppel, D. Rivière, M. Perrot, R. Frackowiak et al., The effect of handedness on the shape of the central sulcus, NeuroImage, vol.60, issue.1, pp.332-339, 2012.
DOI : 10.1016/j.neuroimage.2011.12.050

S. Takerkart and L. Ralaivola, Multiple subject learning for inter-subject prediction, 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.9-12, 2014.
DOI : 10.1109/PRNI.2014.6858548
URL : https://hal.archives-ouvertes.fr/hal-01001987

S. Takerkart, G. Auzias, L. Brun, and O. Coulon, Mapping cortical shape differences using a searchlight approach based on classification of sulcal pit graphs, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015.
DOI : 10.1109/ISBI.2015.7164165
URL : https://hal.archives-ouvertes.fr/hal-01112776

S. Takerkart, G. Auzias, B. Thirion, and L. Ralaivola, Graph-based intersubject pattern analysis of fMRI data, pp.42-111, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01027769

S. Takerkart, G. Auzias, B. Thirion, D. Schön, and L. Ralaivola, Graph-Based Inter-subject Classification of Local fMRI Patterns, pp.184-192
DOI : 10.1007/978-3-642-35428-1_23
URL : https://hal.archives-ouvertes.fr/hal-00818815

T. M. Talavage, Tonotopic Organization in Human Auditory Cortex Revealed by Progressions of Frequency Sensitivity, Journal of Neurophysiology, vol.91, issue.3, pp.1282-1296, 2003.
DOI : 10.1152/jn.01125.2002

B. Tan, E. Zhong, E. W. Xiang, and Q. Yang, Multi-transfer: Transfer learning with multiple views and multiple sources, Statistical Analysis and Data Mining: The ASA Data Science Journal, vol.22, issue.10, 2014.
DOI : 10.1002/sam.11226
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.310.527

M. Tervaniemi and K. Hugdahl, Lateralization of auditory-cortex functions, Brain Research Reviews, vol.43, issue.3, pp.231-246, 2003.
DOI : 10.1016/j.brainresrev.2003.08.004

B. Thirion, P. Pinel, A. Tucholka, A. Roche, P. Ciuciu et al., Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies, IEEE Transactions on Medical Imaging, vol.26, issue.9, pp.1256-1269, 2007.
DOI : 10.1109/TMI.2007.903226
URL : https://hal.archives-ouvertes.fr/cea-00333747

B. Thirion, P. Pinel, and J. Poline, Finding Landmarks in the Functional Brain: Detection and Use for Group Characterization, Medical Image Computing and Computer-Assisted Intervention?MICCAI 2005, pp.476-483, 2005.
DOI : 10.1007/11566489_59

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large margin methods for structured and interdependent output variables, Journal of Machine Learning Research, pp.1453-1484, 2005.

T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends?? in Computer Graphics and Vision, vol.3, issue.3, pp.177-280, 2007.
DOI : 10.1561/0600000017

D. C. Van-essen, M. F. Glasser, D. L. Dierker, J. Harwell, and T. Coalson, Parcellations and Hemispheric Asymmetries of Human Cerebral Cortex Analyzed on Surface-Based Atlases, Cerebral Cortex, vol.22, issue.10, pp.2241-2262, 2012.
DOI : 10.1093/cercor/bhr291

D. C. Van-essen, H. A. Drury, S. Joshi, and M. I. Miller, Functional and structural mapping of human cerebral cortex: Solutions are in the surfaces, Proceedings of the National Academy of Sciences, pp.788-795, 1998.
DOI : 10.1073/pnas.95.3.788

S. Vega-pons and P. Avesani, Brain Decoding via Graph Kernels, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.136-139, 2013.
DOI : 10.1109/PRNI.2013.43

S. Vega-pons, P. Avesani, M. Andric, and U. Hasson, Classification of inter-subject fMRI data based on graph kernels, 2014 International Workshop on Pattern Recognition in Neuroimaging, pp.5-8, 2014.
DOI : 10.1109/PRNI.2014.6858549

S. V. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt, Graph kernels, The Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010.

X. Wang, R. Hutchinson, and T. M. Mitchell, Training fMRI Classifiers to Discriminate Cognitive States across Multiple Subjects, Proc. Sixteenth NIPS Conference, 2004.

. Wikipedia, Error function In: https://en.wikipedia.org/wiki/Error f unction (2015) (cit, p.84

K. J. Worsley, A. C. Evans, S. Marrett, and P. Neelin, 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

X. Yang, Q. Song, and Y. Wang, A WEIGHTED SUPPORT VECTOR MACHINE FOR DATA CLASSIFICATION, International Journal of Pattern Recognition and Artificial Intelligence, vol.21, issue.05, pp.5-961, 2007.
DOI : 10.1142/S0218001407005703

B. Yao and L. Fei-fei, Grouplet: A structured image representation for recognizing human and object interactions, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.9-16, 2010.
DOI : 10.1109/CVPR.2010.5540234

Y. Yao and G. Doretto, Boosting for transfer learning with multiple sources, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1855-1862, 2010.
DOI : 10.1109/CVPR.2010.5539857

L. Yuan, Y. Wang, P. M. Thompson, V. A. Narayan, and J. Ye, Multisource feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data, In: NeuroImage, vol.613, pp.622-632, 2012.

B. Zadrozny, Learning and evaluating classifiers under sample selection bias, Twenty-first international conference on Machine learning , ICML '04, pp.114-150, 2004.
DOI : 10.1145/1015330.1015425
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.6557

H. Zhang, T. Nichols, and T. Johnson, Cluster mass inference via random field theory, NeuroImage, vol.44, issue.1, pp.51-61, 2009.
DOI : 10.1016/j.neuroimage.2008.08.017

L. Zhang and D. Samaras, Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.1211-1217, 2005.
DOI : 10.1109/CVPR.2005.219

Z. Zhao and H. Liu, Multi-Source Feature Selection via Geometry-Dependent Covariance Analysis, In: FSDM, pp.36-47, 2008.

F. Zhuang, X. Cheng, S. Pan, W. Yu, Q. He et al., Transfer Learning with Multiple Sources via Consensus Regularized Autoencoders, Lecture Notes in Computer Science, vol.8726, pp.417-431, 2014.
DOI : 10.1007/978-3-662-44845-8_27