Data heterogeneity consideration in semi-supervised learning
暂无分享,去创建一个
[1] Ronald Rosenfeld,et al. Semi-supervised learning with graphs , 2005 .
[2] Xiaojin Zhu,et al. --1 CONTENTS , 2006 .
[3] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[4] Alexander Zien,et al. Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.
[5] L. Freeman. Centrality in social networks conceptual clarification , 1978 .
[6] Dmitrij Frishman,et al. MIPS: analysis and annotation of proteins from whole genomes in 2005 , 2006, Nucleic Acids Res..
[7] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[8] Olaf Sporns,et al. Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.
[9] Yuan Qi,et al. Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification , 2005, NIPS.
[10] Claire Cardie,et al. Constrained K-means Clustering with Background Knowledge , 2001, ICML.
[11] Celso André R. de Sousa,et al. Influence of Graph Construction on Semi-supervised Learning , 2013, ECML/PKDD.
[12] Fei Wang,et al. Label Propagation through Linear Neighborhoods , 2008, IEEE Trans. Knowl. Data Eng..
[13] Alessandro Vespignani,et al. Epidemic spreading in scale-free networks. , 2000, Physical review letters.
[14] Filipi Nascimento Silva,et al. Hierarchical Characterization of Complex Networks , 2004, cond-mat/0412761.
[15] Rajeev Motwani,et al. The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.
[16] M E J Newman,et al. Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[17] Miguel Á. Carreira-Perpiñán,et al. Proximity Graphs for Clustering and Manifold Learning , 2004, NIPS.
[18] Stephen P. Boyd,et al. The Fastest Mixing Markov Process on a Graph and a Connection to a Maximum Variance Unfolding Problem , 2006, SIAM Rev..
[19] Yamir Moreno,et al. Absence of influential spreaders in rumor dynamics , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[20] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[21] Liang Zhao,et al. A nonparametric classification method based on K-associated graphs , 2011, Inf. Sci..
[22] Ulrik Brandes,et al. Network Analysis: Methodological Foundations (Lecture Notes in Computer Science) , 2005 .
[23] Alessandro Vespignani,et al. Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. , 2003, Physical review letters.
[24] Zhi-Hua Zhou,et al. Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.
[25] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[26] Santo Fortunato,et al. Community detection in graphs , 2009, ArXiv.
[27] Alneu de Andrade Lopes,et al. Graph Construction Based on Labeled Instances for Semi-supervised Learning , 2014, 2014 22nd International Conference on Pattern Recognition.
[28] Xinhua Zhang,et al. Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms , 2006, NIPS.
[29] Seth Pettie,et al. A Randomized Time-Work Optimal Parallel Algorithm for Finding a Minimum Spanning Forest , 1999, RANDOM-APPROX.
[30] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[31] Alessandro Vespignani,et al. Dynamical Processes on Complex Networks , 2008 .
[32] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[33] Liang Zhao,et al. Selecting Nodes with Inhomogeneous Profile for Labeling for Network-Based Semi-supervised Learning , 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.
[34] M E J Newman,et al. Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[35] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[36] Yong Li,et al. ATTACK VULNERABILITY OF COMPLEX NETWORKS BASED ON LOCAL INFORMATION , 2007 .
[37] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[38] Heiko Rieger,et al. Random walks on complex networks. , 2004, Physical review letters.
[39] Seth Pettie,et al. Minimizing randomness in minimum spanning tree, parallel connectivity, and set maxima algorithms , 2002, SODA '02.
[40] Ulrik Brandes,et al. Network Analysis: Methodological Foundations , 2010 .
[41] Alessandro Vespignani,et al. Immunization of complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[42] P. Holland,et al. Transitivity in Structural Models of Small Groups , 1971 .
[43] Hamid R. Rabiee,et al. Supervised neighborhood graph construction for semi-supervised classification , 2012, Pattern Recognit..
[44] S. Havlin,et al. Breakdown of the internet under intentional attack. , 2000, Physical review letters.
[45] Adrian Corduneanu,et al. Data-Dependent Regularization , 2006, Semi-Supervised Learning.
[46] Yamir Moreno,et al. Locating privileged spreaders on an online social network. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[47] Ahmad Akbari,et al. An enhanced noise resilient K-associated graph classifier , 2015, Expert Syst. Appl..
[48] Leonard M. Freeman,et al. A set of measures of centrality based upon betweenness , 1977 .
[49] Yijie Han,et al. Concurrent threads and optimal parallel minimum spanning trees algorithm , 2001, JACM.
[50] M E J Newman,et al. Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[51] Leon Danon,et al. Comparing community structure identification , 2005, cond-mat/0505245.
[52] Liang Zhao,et al. Data clustering using controlled consensus in complex networks , 2013, Neurocomputing.
[53] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[54] Cohen,et al. Resilience of the internet to random breakdowns , 2000, Physical review letters.
[55] Mikhail Belkin,et al. Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..
[56] Fadi Dornaika,et al. Graph-based semi-supervised learning with Local Binary Patterns for holistic object categorization , 2014, Expert Syst. Appl..
[57] M. Newman,et al. Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[58] Mikhail Belkin,et al. Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.
[59] Alexander Zien,et al. Data-Dependent Regularization , 2006 .
[60] Stephanie Forrest,et al. Email networks and the spread of computer viruses. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.
[61] F. Radicchi,et al. Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.
[62] Thorsten Joachims,et al. Transductive Learning via Spectral Graph Partitioning , 2003, ICML.
[63] Avrim Blum,et al. Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.
[64] A. Barabasi,et al. Lethality and centrality in protein networks , 2001, Nature.
[65] Benjamin H. Good,et al. Performance of modularity maximization in practical contexts. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.
[66] John C. Platt,et al. Semi-Supervised Learning with Conditional Harmonic Mixing , 2006, Semi-Supervised Learning.
[67] Albert-László Barabási,et al. Error and attack tolerance of complex networks , 2000, Nature.
[68] Francis Y. L. Chin,et al. Algorithms for Updating Minimal Spanning Trees , 1978, J. Comput. Syst. Sci..
[69] Liang Zhao,et al. Detecting and labeling representative nodes for network-based semi-supervised learning , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[70] Kilian Q. Weinberger,et al. Unsupervised Learning of Image Manifolds by Semidefinite Programming , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[71] Leo Katz,et al. A new status index derived from sociometric analysis , 1953 .
[72] Bernhard Schölkopf,et al. Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.
[73] Nicolas Le Roux,et al. Efficient Non-Parametric Function Induction in Semi-Supervised Learning , 2004, AISTATS.
[74] Shuicheng Yan,et al. Semi-supervised Learning by Sparse Representation , 2009, SDM.
[75] M. Cugmas,et al. On comparing partitions , 2015 .
[76] Shih-Fu Chang,et al. Graph construction and b-matching for semi-supervised learning , 2009, ICML '09.