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» Learning SVMs from Sloppily Labeled Data
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NLE
2008
140views more  NLE 2008»
13 years 9 months ago
Active learning and logarithmic opinion pools for HPSG parse selection
For complex tasks such as parse selection, the creation of labelled training sets can be extremely costly. Resource-efficient schemes for creating informative labelled material mu...
Jason Baldridge, Miles Osborne
ESANN
2006
13 years 10 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...
CORR
2011
Springer
211views Education» more  CORR 2011»
13 years 27 days ago
Labeling 3D scenes for Personal Assistant Robots
—Inexpensive RGB-D cameras that give an RGB image together with depth data have become widely available. We use this data to build 3D point clouds of a full scene. In this paper,...
Hema Swetha Koppula, Abhishek Anand, Thorsten Joac...
JAIR
2010
147views more  JAIR 2010»
13 years 4 months ago
Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction
The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents. To effectivel...
Muhammad Arshad Ul Abedin, Vincent Ng, Latifur Kha...
KDD
2008
ACM
259views Data Mining» more  KDD 2008»
14 years 9 months ago
Using ghost edges for classification in sparsely labeled networks
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...