TAL
2010
Springer
13 years 10 months ago
2010
Springer
Abstract. Most common feature selection techniques for document categorization are supervised and require lots of training data in order to accurately capture the descriptive and d...
ML
2000
ACM
14 years 6 days ago
2000
ACM
Bagging and boosting reduce error by changing both the inputs and outputs to form perturbed training sets, grow predictors on these perturbed training sets and combine them. A que...
COLING
2000
14 years 1 months ago
2000
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
BMVC
2001
14 years 2 months ago
2001
Statistical shape models have been used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a set of dense correspo...
DRR
2010
14 years 2 months ago
2010
Scaling up document-image classifiers to handle an unlimited variety of document and image types poses serious challenges to conventional trainable classifier technologies. Highly...
CIKM
2006
Springer
14 years 4 months ago
2006
Springer
In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is...
ICDAR
2005
IEEE
14 years 6 months ago
2005
IEEE
Recognition of unconstrained handwritten text is still a challenge. In this paper we consider a new problem, which is the recognition of notes written on a whiteboard. Our recogni...
MICAI
2007
Springer
14 years 6 months ago
2007
Springer
A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real...
PKDD
2009
Springer
14 years 7 months ago
2009
Springer
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. ...
MLDM
2009
Springer
14 years 7 months ago
2009
Springer
A number of two-class classification methods first discretize each attribute of two given training sets and then construct a propositional DNF formula that evaluates to True for ...