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ASC
2011
13 years 4 months ago
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact tha...
Edwin Lughofer, Plamen P. Angelov
EMNLP
2007
13 years 10 months ago
Structural Correspondence Learning for Dependency Parsing
Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency parsing (McDonald et al., 2005). To induce the corre...
Nobuyuki Shimizu, Hiroshi Nakagawa
IJCAI
2007
13 years 10 months ago
Optimistic Active-Learning Using Mutual Information
An “active learning system” will sequentially decide which unlabeled instance to label, with the goal of efficiently gathering the information necessary to produce a good cla...
Yuhong Guo, Russell Greiner
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
13 years 10 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà
TNN
2010
159views Management» more  TNN 2010»
13 years 3 months ago
Multiple incremental decremental learning of support vector machines
We propose a multiple incremental decremental algorithm of Support Vector Machine (SVM). Conventional single incremental decremental SVM can update the trained model efficiently w...
Masayuki Karasuyama, Ichiro Takeuchi