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» Margin based feature selection - theory and algorithms
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PERCOM
2010
ACM
13 years 6 months ago
All for one or one for all? Combining heterogeneous features for activity spotting
Abstract—Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work ar...
Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lu...
SAC
2006
ACM
14 years 1 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal
NIPS
2007
13 years 9 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
CVPR
2012
IEEE
11 years 10 months ago
Beyond spatial pyramids: Receptive field learning for pooled image features
In this paper we examine the effect of receptive field designs on classification accuracy in the commonly adopted pipeline of image classification. While existing algorithms us...
Yangqing Jia, Chang Huang, Trevor Darrell
TIP
2010
205views more  TIP 2010»
13 years 2 months ago
Variational Region-Based Segmentation Using Multiple Texture Statistics
This paper addresses variational supervised texture segmentation. The main contributions are twofold. First, the proposed method circumvents a major problem related to classical t...
Imen Karoui, Ronan Fablet, Jean-Marc Boucher, Jean...