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» On the Learnability of Vector Spaces
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SSPR
2010
Springer
15 years 4 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...
CVPR
2009
IEEE
17 years 1 months ago
Holistic Context Modeling using Semantic Co-occurrences
We present a simple framework to model contextual relationships between visual concepts. The new framework combines ideas from previous object-centric methods (which model conte...
Nikhil Rasiwasia (University Of California, San Di...
CVPR
2004
IEEE
16 years 8 months ago
Random Sampling LDA for Face Recognition
Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with t...
Xiaogang Wang, Xiaoou Tang
CVPR
2005
IEEE
16 years 8 months ago
Fisher+Kernel Criterion for Discriminant Analysis
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...
Shu Yang, Shuicheng Yan, Dong Xu, Xiaoou Tang, Cha...
CVPR
2007
IEEE
16 years 8 months ago
Image Hallucination Using Neighbor Embedding over Visual Primitive Manifolds
In this paper, we propose a novel learning-based method for image hallucination, with image super-resolution being a specific application that we focus on here. Given a low-resolu...
Wei Fan, Dit-Yan Yeung