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ML
2008
ACM
15 years 6 months ago
Margin-based first-order rule learning
Abstract We present a new margin-based approach to first-order rule learning. The approach addresses many of the prominent challenges in first-order rule learning, such as the comp...
Ulrich Rückert, Stefan Kramer
CVPR
2008
IEEE
16 years 8 months ago
Learning and using taxonomies for fast visual categorization
The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously Ncat = 104 -...
Gregory Griffin, Darya Perona
ICCV
2003
IEEE
16 years 8 months ago
Learning a Classification Model for Segmentation
We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly m...
Xiaofeng Ren, Jitendra Malik
IJCNN
2007
IEEE
16 years 27 days ago
A Neural Network-based Learning Controller for Micro-sized Object Micromanipulation
Abstract— In this paper, automated micro-sized objects manipulation is investigated. The novelty of the proposed method lies on the compensation of all the nonlinear scaling forc...
Mohsen Shahini, William W. Melek, John T. W. Yeow
ICVS
2003
Springer
15 years 11 months ago
A Spectral Approach to Learning Structural Variations in Graphs
This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix. We commence b...
Bin Luo, Richard C. Wilson, Edwin R. Hancock