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» Multi-label learning by exploiting label dependency
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CVPR
2012
IEEE
11 years 7 months ago
Structured Local Predictors for image labelling
In this paper we introduce Structured Local Predictors (SLP) – A new formulation that considers the image labelling problem from a structured learning point of view. SLP are loc...
Samuel Rota Bulò, Peter Kontschieder, Marce...
ICCV
2011
IEEE
12 years 4 months ago
Actively Selecting Annotations Among Objects and Attributes
We present an active learning approach to choose image annotation requests among both object category labels and the objects’ attribute labels. The goal is to solicit those labe...
Adriana Kovashka, Sudheendra Vijayanarasimhan, Kri...
ICDM
2007
IEEE
162views Data Mining» more  ICDM 2007»
13 years 8 months ago
Exploiting Network Structure for Active Inference in Collective Classification
Active inference seeks to maximize classification performance while minimizing the amount of data that must be labeled ex ante. This task is particularly relevant in the context o...
Matthew J. Rattigan, Marc Maier, David Jensen, Bin...
NIPS
2003
13 years 6 months ago
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial depe...
Sanjiv Kumar, Martial Hebert
ECML
2006
Springer
13 years 8 months ago
Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees
In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kerne...
Alessandro Moschitti