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» Learning aspect models with partially labeled data
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ICML
2003
IEEE
16 years 4 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
ICML
2006
IEEE
16 years 4 months ago
Combining discriminative features to infer complex trajectories
We propose a new model for the probabilistic estimation of continuous state variables from a sequence of observations, such as tracking the position of an object in video. This ma...
David A. Ross, Simon Osindero, Richard S. Zemel
ICDM
2010
IEEE
154views Data Mining» more  ICDM 2010»
15 years 2 months ago
Discrimination Aware Decision Tree Learning
Abstract--Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute , find a classifier with high predictive accu...
Faisal Kamiran, Toon Calders, Mykola Pechenizkiy
JAIR
2010
147views more  JAIR 2010»
14 years 11 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...
ESANN
2006
15 years 5 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...