Nearest hyperdisk methods for high-dimensional classification
暂无分享,去创建一个
[1] Geoffrey E. Hinton,et al. Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.
[2] Jorma T. Laaksonen. Subspace Classifiers in Recognition of Handwritten Digits , 1997 .
[3] Bernhard Schölkopf,et al. Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.
[4] Kristin P. Bennett,et al. Duality and Geometry in SVM Classifiers , 2000, ICML.
[5] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[6] M. Bilginer Gülmezoglu,et al. The common vector approach and its relation to principal component analysis , 2001, IEEE Trans. Speech Audio Process..
[7] Pascal Vincent,et al. K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms , 2001, NIPS.
[8] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[9] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[10] Cordelia Schmid,et al. A maximum entropy framework for part-based texture and object recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[11] Hakan Cevikalp,et al. Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Leon N. Cooper,et al. Pattern Classification via Single Spheres , 2005, Discovery Science.
[13] P. Groenen,et al. Nearest convex hull classification , 2006 .
[14] Jakob J. Verbeek,et al. Learning nonlinear image manifolds by global alignment of local linear models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] M. Douze,et al. Local Subspace Classifiers: Linear and Nonlinear Approaches , 2007, 2007 IEEE Workshop on Machine Learning for Signal Processing.
[16] Hakan Cevikalp,et al. Margin-based discriminant dimensionality reduction for visual recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.