The Locally Weighted Bag of Words Framework for Document Representation
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[1] J. Munkres. ALGORITHMS FOR THE ASSIGNMENT AND TRANSIORTATION tROBLEMS* , 1957 .
[2] R. Kass. The Geometry of Asymptotic Inference , 1989 .
[3] M. Spivak. A comprehensive introduction to differential geometry , 1979 .
[4] Hinrich Schütze,et al. Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.
[5] John D. Lafferty,et al. Information Diffusion Kernels , 2002, NIPS.
[6] John D. Lafferty,et al. Diffusion Kernels on Statistical Manifolds , 2005, J. Mach. Learn. Res..
[7] W. Boothby. An introduction to differentiable manifolds and Riemannian geometry , 1975 .
[8] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[9] Yoram Singer,et al. BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.
[10] S. Chiba,et al. Dynamic programming algorithm optimization for spoken word recognition , 1978 .
[11] Yiming Yang,et al. An Evaluation of Statistical Approaches to Text Categorization , 1999, Information Retrieval.
[12] Yang Zhao,et al. Local likelihood modeling of the concept drift phenomenon , 2009 .
[13] Risi Kondor,et al. Diffusion kernels on graphs and other discrete structures , 2002, ICML 2002.
[14] M. Wand. Local Regression and Likelihood , 2001 .
[15] John D. Lafferty,et al. Statistical Models for Text Segmentation , 1999, Machine Learning.
[16] Guohua Pan,et al. Local Regression and Likelihood , 1999, Technometrics.
[17] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[18] Thorsten Joachims,et al. The Maximum-Margin Approach to Learning Text Classifiers , 2001, Künstliche Intell..
[19] John D. Lafferty,et al. Dynamic topic models , 2006, ICML.
[20] George Forman,et al. Tackling concept drift by temporal inductive transfer , 2006, SIGIR.
[21] L. Campbell. An extended Čencov characterization of the information metric , 1986 .
[22] R. Dudley. A course on empirical processes , 1984 .
[23] Tong Zhang,et al. Covering Number Bounds of Certain Regularized Linear Function Classes , 2002, J. Mach. Learn. Res..
[24] Charles R. Johnson,et al. Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.
[25] M. Berger,et al. Le Spectre d'une Variete Riemannienne , 1971 .
[26] Bernhard Schölkopf,et al. Generalization Performance of Regularization Networks and Support Vector Machines via Entropy Numbers of Compact Operators , 1998 .
[27] Marco Cuturi. Learning from Structured Objects with Semigroup Kernels , 2006 .
[28] Lucy T. Nowell,et al. ThemeRiver: Visualizing Thematic Changes in Large Document Collections , 2002, IEEE Trans. Vis. Comput. Graph..
[29] Dunja Mladenic,et al. Visualization of Text Document Corpus , 2005, Informatica.
[30] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[31] John Shawe-Taylor,et al. Covering numbers for support vector machines , 1999, COLT '99.
[32] Alan Thornton Gous,et al. Exponential and spherical subfamily models , 1998 .
[33] John M. Lee. Introduction to Smooth Manifolds , 2002 .
[34] G. Terrell. Statistical theory and computational aspects of smoothing , 1997 .
[35] Tomoko Matsui,et al. A Kernel for Time Series Based on Global Alignments , 2006, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[36] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[37] N. Čencov. Statistical Decision Rules and Optimal Inference , 2000 .
[38] Thomas Hofmann,et al. Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval , 2000, ICML.
[39] J. Ramsay,et al. Some Tools for Functional Data Analysis , 1991 .
[40] Yi Mao,et al. Sequential Document Visualization , 2007, IEEE Transactions on Visualization and Computer Graphics.
[41] John D. Lafferty,et al. A study of smoothing methods for language models applied to Ad Hoc information retrieval , 2001, SIGIR '01.
[42] Matthias Hein,et al. Hilbertian Metrics and Positive Definite Kernels on Probability Measures , 2005, AISTATS.
[43] Frederick Jelinek,et al. Statistical methods for speech recognition , 1997 .
[44] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[45] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[46] R. Kass,et al. Geometrical Foundations of Asymptotic Inference , 1997 .
[47] Ronald Rosenfeld,et al. A survey of smoothing techniques for ME models , 2000, IEEE Trans. Speech Audio Process..
[48] Tong Zhang,et al. Text Categorization Based on Regularized Linear Classification Methods , 2001, Information Retrieval.