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» Learning Classifiers from Semantically Heterogeneous Data
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KDD
2001
ACM
216views Data Mining» more  KDD 2001»
14 years 9 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
JMLR
2012
11 years 11 months ago
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its s...
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu...
AIME
1997
Springer
14 years 1 months ago
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244...
William Rodman Shankle, Subramani Mani, Michael J....
AIRS
2010
Springer
13 years 7 months ago
Semantic Relation Extraction Based on Semi-supervised Learning
Many tasks of information extraction or natural language processing have a property that the data naturally consist of several views--disjoint subsets of features. Specifically, a ...
Haibo Li, Yutaka Matsuo, Mitsuru Ishizuka
ICC
2009
IEEE
107views Communications» more  ICC 2009»
14 years 3 months ago
Dynamic Resource Modeling for Heterogeneous Wireless Networks
— High variability of access resources in heterogenous wireless networks and limited computing power and battery life of mobile computing devices such as smartphones call for nov...
Dimitrios Tsamis, Tansu Alpcan, Jatinder Pal Singh...