Discriminative Feature Selection by Nonparametric Bayes Error Minimization
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[1] Igor Kononenko,et al. Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.
[2] Hiroshi Mamitsuka,et al. Query-learning-based iterative feature-subset selection for learning from high-dimensional data sets , 2005, Knowledge and Information Systems.
[3] Michael I. Jordan,et al. Feature selection for high-dimensional genomic microarray data , 2001, ICML.
[4] Deniz Erdogmus,et al. Feature extraction using information-theoretic learning , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Zheng Bao,et al. Large Margin Feature Weighting Method via Linear Programming , 2009, IEEE Transactions on Knowledge and Data Engineering.
[6] David W. Aha,et al. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms , 1997, Artificial Intelligence Review.
[7] Marko Robnik-Sikonja,et al. Comprehensible Interpretation of Relief's Estimates , 2001, ICML.
[8] Larry A. Rendell,et al. A Practical Approach to Feature Selection , 1992, ML.
[9] Nuno Vasconcelos. Feature selection by maximum marginal diversity: optimality and implications for visual recognition , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[10] Shuang-Hong Yang,et al. Language pyramid and multi-scale text analysis , 2010, CIKM.
[11] Hans-Peter Kriegel,et al. Feature Weighting and Instance Selection for Collaborative Filtering: An Information-Theoretic Approach* , 2003, Knowledge and Information Systems.
[12] Geoff Holmes,et al. Benchmarking Attribute Selection Techniques for Discrete Class Data Mining , 2003, IEEE Trans. Knowl. Data Eng..
[13] Daphne Koller,et al. Toward Optimal Feature Selection , 1996, ICML.
[14] Yijun Sun,et al. Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Marko Robnik-Sikonja,et al. Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.
[16] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[17] Runze Li,et al. Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery , 2006, math/0602133.
[18] Shaohua Kevin Zhou,et al. Variational Graph Embedding for Globally and Locally Consistent Feature Extraction , 2009, ECML/PKDD.
[19] Shuang-Hong Yang,et al. Feature Selection by Nonparametric Bayes Error Minimization , 2008, PAKDD.
[20] U. Feige,et al. Spectral Graph Theory , 2015 .
[21] Gustavo Carneiro,et al. Minimum Bayes error features for visual recognition by sequential feature selection and extraction , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).
[22] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[23] Shuang-Hong Yang,et al. Efficient Feature Selection in the Presence of Outliers and Noises , 2008, AIRS.
[24] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[25] Huan Liu,et al. Feature Selection for Classification , 1997, Intell. Data Anal..
[26] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[27] George Saon,et al. Minimum Bayes Error Feature Selection for Continuous Speech Recognition , 2000, NIPS.
[28] Cyrus Shahabi,et al. Feature subset selection and feature ranking for multivariate time series , 2005, IEEE Transactions on Knowledge and Data Engineering.
[29] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.
[30] Naftali Tishby,et al. Margin based feature selection - theory and algorithms , 2004, ICML.
[31] Kari Torkkola,et al. Feature Extraction by Non-Parametric Mutual Information Maximization , 2003, J. Mach. Learn. Res..
[32] Anil K. Jain,et al. Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[33] Huan Liu,et al. Efficient Feature Selection via Analysis of Relevance and Redundancy , 2004, J. Mach. Learn. Res..
[34] P. Bickel,et al. SIMULTANEOUS ANALYSIS OF LASSO AND DANTZIG SELECTOR , 2008, 0801.1095.
[35] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[36] José Ranilla,et al. Introducing a family of linear measures for feature selection in text categorization , 2005, IEEE Transactions on Knowledge and Data Engineering.
[37] Bernhard Schölkopf,et al. Use of the Zero-Norm with Linear Models and Kernel Methods , 2003, J. Mach. Learn. Res..
[38] David R. Musicant,et al. Lagrangian Support Vector Machines , 2001, J. Mach. Learn. Res..
[39] Glenn Fung,et al. SVM Feature Selection for Classification of SPECT Images of Alzheimer's Disease Using Spatial Information , 2005, ICDM.
[40] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[41] Yun Q. Shi,et al. Feature Selection based on the Bhattacharyya Distance , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[42] Huan Liu,et al. Spectral feature selection for supervised and unsupervised learning , 2007, ICML '07.
[43] Keinosuke Fukunaga,et al. Bayes Error Estimation Using Parzen and k-NN Procedures , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Yiming Yang,et al. A re-examination of text categorization methods , 1999, SIGIR '99.
[45] Qingfeng Chen,et al. Discovery of Structural and Functional Features in RNA Pseudoknots , 2009, IEEE Transactions on Knowledge and Data Engineering.
[46] Salim Hariri,et al. A new dependency and correlation analysis for features , 2005, IEEE Transactions on Knowledge and Data Engineering.
[47] Chulhee Lee,et al. Feature extraction based on the Bhattacharyya distance , 2003, Pattern Recognit..
[48] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[49] David G. Stork,et al. Pattern Classification , 1973 .
[50] Mikhail Belkin,et al. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.