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ICDM
2003
IEEE
143views Data Mining» more  ICDM 2003»
14 years 1 months ago
Active Sampling for Feature Selection
In knowledge discovery applications, where new features are to be added, an acquisition policy can help select the features to be acquired based on their relevance and the cost of...
Sriharsha Veeramachaneni, Paolo Avesani
ICDAR
2011
IEEE
12 years 7 months ago
Continuous CRF with Multi-scale Quantization Feature Functions Application to Structure Extraction in Old Newspaper
—We introduce quantization feature functions to represent continuous or large range discrete data into the symbolic CRF data representation. We show that doing this convertion in...
David Hebert, Thierry Paquet, Stéphane Nico...
ICCV
2011
IEEE
12 years 8 months ago
Feature Seeding for Action Recognition
Progress in action recognition has been in large part due to advances in the features that drive learning-based methods. However, the relative sparsity of training data and the ri...
Pyry Matikainen, Rahul Sukthankar, Martial Hebert
ICML
2010
IEEE
13 years 9 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
ICDAR
2009
IEEE
13 years 5 months ago
Combining Multiple HMMs Using On-line and Off-line Features for Off-line Arabic Handwriting Recognition
This paper presents an off-line Arabic Handwriting recognition system based on the selection of different state of the art features and the combination of multiple Hidden Markov M...
Mahdi Hamdani, Haikal El Abed, Monji Kherallah, Ad...