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» Incremental Dependency Parsing Using Online Learning
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110
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KDD
1999
ACM
199views Data Mining» more  KDD 1999»
15 years 6 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
ECML
2007
Springer
15 years 8 months ago
Weighted Kernel Regression for Predicting Changing Dependencies
Abstract. Consider the online regression problem where the dependence of the outcome yt on the signal xt changes with time. Standard regression techniques, like Ridge Regression, d...
Steven Busuttil, Yuri Kalnishkan
EMNLP
2009
14 years 11 months ago
Unsupervised Semantic Parsing
We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, r...
Hoifung Poon, Pedro Domingos
CVPR
2005
IEEE
16 years 3 months ago
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
This paper presents an online learning algorithm to construct from video sequences an image-based representation that is useful for recognition and tracking. For a class of object...
Kuang-Chih Lee, David J. Kriegman
136
Voted
CVPR
2004
IEEE
16 years 3 months ago
An Unsupervised, Online Learning Framework for Moving Object Detection
Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the ...
Vinod Nair, James J. Clark