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NN
2008
Springer
143views Neural Networks» more  NN 2008»
14 years 10 months ago
A batch ensemble approach to active learning with model selection
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
Masashi Sugiyama, Neil Rubens
ICML
2004
IEEE
15 years 10 months ago
Large margin hierarchical classification
We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded b...
Ofer Dekel, Joseph Keshet, Yoram Singer
ICML
2004
IEEE
15 years 10 months ago
Improving SVM accuracy by training on auxiliary data sources
The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second...
Pengcheng Wu, Thomas G. Dietterich
ICDM
2009
IEEE
97views Data Mining» more  ICDM 2009»
15 years 4 months ago
Hierarchical Probabilistic Segmentation of Discrete Events
—Segmentation, the task of splitting a long sequence of discrete symbols into chunks, can provide important information about the nature of the sequence that is understandable to...
Guy Shani, Christopher Meek, Asela Gunawardana
PRIB
2009
Springer
209views Bioinformatics» more  PRIB 2009»
15 years 4 months ago
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. An established approach to pursuing supervised learning with ...
Yiming Ying, Colin Campbell, Theodoros Damoulas, M...