Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization
暂无分享,去创建一个
Bo Zhang | Jun Zhu | Hang Su | Jingwei Zhuo | Wenbo Hu
[1] Dinh Phung,et al. Journal of Machine Learning Research: Preface , 2014 .
[2] Bernhard Schölkopf,et al. Introduction to Semi-Supervised Learning , 2006, Semi-Supervised Learning.
[3] Stephen E. Fienberg,et al. Discriminative Topic Modeling Based on Manifold Learning , 2012, ACM Trans. Knowl. Discov. Data.
[4] Tong Zhang,et al. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding , 2015, NIPS.
[5] Eric P. Xing,et al. MedLDA: maximum margin supervised topic models , 2012, J. Mach. Learn. Res..
[6] Susan T. Dumais,et al. Partially labeled topic models for interpretable text mining , 2011, KDD.
[7] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[8] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[9] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[10] Michael I. Jordan,et al. DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification , 2008, NIPS.
[11] Jun Zhu,et al. Fast Sampling for Bayesian Max-Margin Models , 2015 .
[12] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[13] Wei Wei,et al. A jointly distributed semi-supervised topic model , 2014, Neurocomputing.
[14] Yaoliang Yu,et al. Linear Time Samplers for Supervised Topic Models using Compositional Proposals , 2015, KDD.
[15] François Laviolette,et al. PAC-Bayesian learning of linear classifiers , 2009, ICML '09.
[16] Ramesh Nallapati,et al. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora , 2009, EMNLP.
[17] David A. McAllester. PAC-Bayesian Stochastic Model Selection , 2003, Machine Learning.
[18] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[19] Yee Whye Teh,et al. Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex , 2013, NIPS.
[20] Tong Zhang,et al. Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings , 2016, ICML.
[21] Chong Wang,et al. Simultaneous image classification and annotation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Nenghai Yu,et al. Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learning , 2011, Neurocomputing.
[23] Deng Cai,et al. Probabilistic dyadic data analysis with local and global consistency , 2009, ICML '09.
[24] David M. Blei,et al. Supervised Topic Models , 2007, NIPS.
[25] Ning Chen,et al. Bayesian inference with posterior regularization and applications to infinite latent SVMs , 2012, J. Mach. Learn. Res..
[26] Ming Li,et al. Online Manifold Regularization: A New Learning Setting and Empirical Study , 2008, ECML/PKDD.
[27] Koby Crammer,et al. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..
[28] Thorsten Joachims,et al. Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.
[29] Hongfei Yan,et al. SSHLDA: A Semi-Supervised Hierarchical Topic Model , 2012, EMNLP.
[30] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[31] Katsumi Nitta,et al. Semi-supervised Latent Dirichlet Allocation for Multi-label Text Classification , 2013, IEA/AIE.
[32] Ning Chen,et al. Gibbs max-margin topic models with data augmentation , 2013, J. Mach. Learn. Res..