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» Learning Classifiers from Semantically Heterogeneous Data
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CVPR
2006
IEEE
14 years 9 months ago
A Conic Section Classifier and its Application to Image Datasets
Many problems in computer vision involving recognition and/or classification can be posed in the general framework of supervised learning. There is however one aspect of image dat...
Arunava Banerjee, Santhosh Kodipaka, Baba C. Vemur...
IJDMB
2007
110views more  IJDMB 2007»
13 years 7 months ago
Transductive learning with EM algorithm to classify proteins based on phylogenetic profiles
: Phylogenetic profiles of proteins  strings of ones and zeros encoding respectively the presence and absence of proteins in a group of genomes  have recently been used to id...
Roger A. Craig, Li Liao
FLAIRS
2008
13 years 10 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
ECML
2005
Springer
14 years 1 months ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Xiaoli Li, Bing Liu
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han