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» Predicting labels for dyadic data
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EMMCVPR
2011
Springer
12 years 9 months ago
Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies: An ImageNet Case Study
The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. However, these annotations may be imperfect, in the sense t...
Julian John McAuley, Arnau Ramisa, Tibério ...
ICML
2010
IEEE
13 years 11 months ago
Large Graph Construction for Scalable Semi-Supervised Learning
In this paper, we address the scalability issue plaguing graph-based semi-supervised learning via a small number of anchor points which adequately cover the entire point cloud. Cr...
Wei Liu, Junfeng He, Shih-Fu Chang
EDM
2008
96views Data Mining» more  EDM 2008»
13 years 11 months ago
Labeling Student Behavior Faster and More Precisely with Text Replays
We present text replays, a method for generating labels that can be used to train classifiers of student behavior. We use this method to label data as to whether students are gamin...
Ryan Shaun Joazeiro de Baker, Adriana M. J. B. de ...
SDM
2008
SIAM
121views Data Mining» more  SDM 2008»
13 years 11 months ago
Integration of Multiple Networks for Robust Label Propagation
Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks a...
Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama
ICCS
2007
Springer
14 years 4 months ago
Learning Common Outcomes of Communicative Actions Represented by Labeled Graphs
We build a generic methodology based on learning and reasoning to detect specific attitudes of human agents and patterns of their interactions. Human attitudes are determined in te...
Boris Galitsky, Boris Kovalerchuk, Sergei O. Kuzne...