Fitting a graph to vector data
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[1] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[2] Kim-Chuan Toh,et al. SDPT3 -- A Matlab Software Package for Semidefinite Programming , 1996 .
[3] Stephen J. Wright,et al. Interior-point methods , 2000 .
[4] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[5] Frank McSherry,et al. Spectral partitioning of random graphs , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.
[6] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[7] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[8] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[9] Thorsten Joachims,et al. Transductive Learning via Spectral Graph Partitioning , 2003, ICML.
[10] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.
[11] Kim-Chuan Toh,et al. Solving semidefinite-quadratic-linear programs using SDPT3 , 2003, Math. Program..
[12] B. Schölkopf,et al. A Regularization Framework for Learning from Graph Data , 2004, ICML 2004.
[13] Kai Yu. Semi-supervised Induction with Basis Functions , 2004 .
[14] Shang-Hua Teng,et al. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems , 2003, STOC '04.
[15] Mikhail Belkin,et al. Tikhonov regularization and semi-supervised learning on large graphs , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[16] Mikhail Belkin,et al. Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.
[17] Bernhard Schölkopf,et al. Learning from Labeled and Unlabeled Data Using Random Walks , 2004, DAGM-Symposium.
[18] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[19] Mark Herbster,et al. Combining Graph Laplacians for Semi-Supervised Learning , 2005, NIPS.
[20] M. Maggioni,et al. GEOMETRIC DIFFUSIONS AS A TOOL FOR HARMONIC ANALYSIS AND STRUCTURE DEFINITION OF DATA PART I: DIFFUSION MAPS , 2005 .
[21] Geoffrey I. Webb,et al. Not So Naive Bayes: Aggregating One-Dependence Estimators , 2005, Machine Learning.
[22] Mikhail Belkin,et al. Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.
[23] R. Coifman,et al. Geometric harmonics: A novel tool for multiscale out-of-sample extension of empirical functions , 2006 .
[24] Harry Zhang,et al. Full Bayesian network classifiers , 2006, ICML.
[25] Sotiris B. Kotsiantis,et al. Machine learning: a review of classification and combining techniques , 2006, Artificial Intelligence Review.
[26] Ulrike von Luxburg,et al. Influence of graph construction on graph-based clustering measures , 2008, NIPS.
[27] Shih-Fu Chang,et al. Graph transduction via alternating minimization , 2008, ICML '08.
[28] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.