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SIGIR
2012
ACM
13 years 9 months ago
Top-k learning to rank: labeling, ranking and evaluation
In this paper, we propose a novel top-k learning to rank framework, which involves labeling strategy, ranking model and evaluation measure. The motivation comes from the difficul...
Shuzi Niu, Jiafeng Guo, Yanyan Lan, Xueqi Cheng
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
ICCV
2007
IEEE
16 years 8 months ago
Separating Parts from 2D Shapes using Relatability
It's often important to analyze shapes as made up of parts. But there are two ways to think of how parts fit together. We can characterize the remainder of a shape after a pa...
Xiaofeng Mi, Douglas DeCarlo
168
Voted
ICCV
2007
IEEE
16 years 8 months ago
Fast Pixel/Part Selection with Sparse Eigenvectors
We extend the "Sparse LDA" algorithm of [7] with new sparsity bounds on 2-class separability and efficient partitioned matrix inverse techniques leading to 1000-fold spe...
Bernard Moghaddam, Yair Weiss, Shai Avidan
ICPR
2002
IEEE
16 years 7 months ago
Adaptive Stack Algorithm in Document Image Decoding
The Stack algorithm, which is a best-first search algorithm widely used in speech recognition, is modified for application to the problem of recognizing machine printed text in th...
Kris Popat, Daniel H. Greene, Tze-Lei Poo