ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
The applicationofboosting procedures to decision tree algorithmshas been shown to produce very accurate classi ers. These classiers are in the form of a majority vote over a numbe...
ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior ...
ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
ICML
1999
IEEE
15 years 15 days ago
1999
IEEE
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-...
ICML
2000
IEEE
15 years 15 days ago
2000
IEEE
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being...
ICML
2000
IEEE
15 years 15 days ago
2000
IEEE
ICML
2000
IEEE
15 years 15 days ago
2000
IEEE
Clustering algorithms conduct a search through the space of possible organizations of a data set. In this paper, we propose two types of instance-level clustering constraints ? mu...
ICML
2000
IEEE
15 years 15 days ago
2000
IEEE
If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very n...