NIPS
1997
14 years 1 months ago
1997
Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many ins...
FLAIRS
1998
14 years 1 months ago
1998
Manual generation of training examples for supervised learning is an expensive process. One way to reduce this cost is to produce training instances that are highly informative. T...
COLING
2000
14 years 1 months ago
2000
The goal of text categorization is to classify documents into a certain number of pre-defined categories. The previous works in this area have used a large number of labeled train...
ACL
2004
14 years 1 months ago
2004
Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In ...
VMV
2008
14 years 1 months ago
2008
While supervised learning approaches for 3D shape retrieval have been successfully used to incorporate human knowledge about object classes based on global shape features, the inc...
NIPS
2007
14 years 1 months ago
2007
We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of C...
LREC
2008
14 years 1 months ago
2008
This paper describes Eksairesis, a system for learning economic domain knowledge automatically from Modern Greek text. The knowledge is in the form of economic terms and the seman...
GECCO
2006
Springer
14 years 4 months ago
2006
Springer
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...
IJCNN
2000
IEEE
14 years 4 months ago
2000
IEEE
The problem of designing input signals for optimal generalization in supervised learning is called active learning. In many active learning methods devised so far, the bias of the...
EPIA
2003
Springer
14 years 5 months ago
2003
Springer
Most of supervised learning algorithms assume the stability of the target concept over time. Nevertheless in many real-user modeling systems, where the data is collected over an ex...