Combining information theoretic kernels with generative embeddings for classification
暂无分享,去创建一个
André F. T. Martins | Mário A. T. Figueiredo | Alessandro Perina | Manuele Bicego | Umberto Castellani | Vittorio Murino | Pedro M. Q. Aguiar | Aydin Ulas | A. Perina | Vittorio Murino | U. Castellani | P. Aguiar | M. Bicego | Aydin Ulas
[1] Jean-Cédric Chappelier,et al. PLSI: The True Fisher Kernel and beyond , 2009, ECML/PKDD.
[2] Thomas Hofmann,et al. Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.
[3] Henrik Boström,et al. Fusion of dimensionality reduction methods: A case study in microarray classification , 2009, 2009 12th International Conference on Information Fusion.
[4] Colin Campbell,et al. The latent process decomposition of cDNA microarray data sets , 2005, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[5] Mário A. T. Figueiredo,et al. Hybrid generative/discriminative training of radial basis function networks , 2006, ESANN.
[6] Nebojsa Jojic,et al. Free energy score space , 2009, NIPS.
[7] Robert P. W. Duin,et al. Component-based discriminative classification for hidden Markov models , 2009, Pattern Recognit..
[8] Andrew Zisserman,et al. Scene Classification Via pLSA , 2006, ECCV.
[9] Alessandro Perina,et al. Expression microarray classification using topic models , 2010, SAC '10.
[10] Lei Liu,et al. Ensemble gene selection by grouping for microarray data classification , 2010, J. Biomed. Informatics.
[11] Joachim M. Buhmann,et al. Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma , 2010, DAGM-Symposium.
[12] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[13] Michael I. Jordan,et al. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes , 2001, NIPS.
[14] Manuele Bicego,et al. Hybrid Generative-Discriminative Nucleus Classification of Renal Cell Carcinoma , 2011, SIMBAD.
[15] Nebojsa Jojic,et al. A hybrid generative/discriminative classification framework based on free-energy terms , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[16] U. Alon,et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[17] Guillaume Bouchard,et al. The Tradeoff Between Generative and Discriminative Classifiers , 2004 .
[18] M. Bellani,et al. Thalamic‐insular dysconnectivity in schizophrenia: Evidence from structural equation modeling , 2012, Human brain mapping.
[19] Trevor J. Hastie,et al. Discriminative vs Informative Learning , 1997, KDD.
[20] Mário A. T. Figueiredo,et al. Similarity-based classification of sequences using hidden Markov models , 2004, Pattern Recognit..
[21] Daniel Q. Naiman,et al. Microarray Classification from Several Two-Gene Expression Comparisons , 2008, 2008 Seventh International Conference on Machine Learning and Applications.
[22] Tom Minka,et al. Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[23] Michele Tansella,et al. Brain Morphometry by Probabilistic Latent Semantic Analysis , 2010, MICCAI.
[24] Thomas Hofmann,et al. Learning the Similarity of Documents: An Information-Geometric Approach to Document Retrieval and Categorization , 1999, NIPS.
[25] Tony Jebara,et al. Probability Product Kernels , 2004, J. Mach. Learn. Res..
[26] Li Wang,et al. Hybrid huberized support vector machines for microarray classification , 2007, ICML '07.
[27] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[28] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[29] Michele Tansella,et al. A New Shape Diffusion Descriptor for Brain Classification , 2011, MICCAI.
[30] Eric P. Xing,et al. Nonextensive Information Theoretic Kernels on Measures , 2009, J. Mach. Learn. Res..
[31] Leonidas J. Guibas,et al. A concise and provably informative multi-scale signature based on heat diffusion , 2009 .
[32] Mark J. F. Gales,et al. Using SVMs to classify variable length speech patterns , 2002 .
[33] Gunnar Rätsch,et al. A New Discriminative Kernel from Probabilistic Models , 2001, Neural Computation.
[34] V. Calhoun,et al. Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia , 2005, Schizophrenia Research.
[35] Kenji Fukumizu,et al. Semigroup Kernels on Measures , 2005, J. Mach. Learn. Res..
[36] Naonori Ueda,et al. Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[37] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[38] Tai Sing Lee,et al. Hybrid generative-discriminative classification using posterior divergence , 2011, CVPR 2011.
[39] Michele Tansella,et al. Selecting Scales by Multiple Kernel Learning for Shape Diffusion Analysis , 2011 .
[40] Mark J. F. Gales,et al. Speech Recognition using SVMs , 2001, NIPS.
[41] Andrew Zisserman,et al. Representing shape with a spatial pyramid kernel , 2007, CIVR '07.
[42] Cristian Sminchisescu,et al. Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).