Sciweavers

51 search results - page 1 / 11
» Boosting Optimal Logical Patterns Using Noisy Data
Sort
View
SDM
2007
SIAM
104views Data Mining» more  SDM 2007»
13 years 6 months ago
Boosting Optimal Logical Patterns Using Noisy Data
We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube...
Noam Goldberg, Chung-chieh Shan
MCS
2004
Springer
13 years 10 months ago
Experiments on Ensembles with Missing and Noisy Data
Abstract. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classificati...
Prem Melville, Nishit Shah, Lilyana Mihalkova, Ray...
CVPR
2011
IEEE
13 years 8 days ago
Mining Discriminative Co-occurrence Patterns for Visual Recognition
The co-occurrence pattern, a combination of binary or local features, is more discriminative than individual features and has shown its advantages in object, scene, and action rec...
Junsong Yuan, Ming Yang, Ying Wu
DIS
2010
Springer
13 years 2 months ago
Sparse Substring Pattern Set Discovery Using Linear Programming Boosting
In this paper, we consider finding a small set of substring patterns which classifies the given documents well. We formulate the problem as 1 norm soft margin optimization problem ...
Kazuaki Kashihara, Kohei Hatano, Hideo Bannai, Mas...
GECCO
2004
Springer
127views Optimization» more  GECCO 2004»
13 years 10 months ago
Improved Niching and Encoding Strategies for Clustering Noisy Data Sets
Clustering is crucial to many applications in pattern recognition, data mining, and machine learning. Evolutionary techniques have been used with success in clustering, but most su...
Olfa Nasraoui, Elizabeth Leon