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EMNLP
2007
15 years 5 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
123
Voted
IJCAI
2007
15 years 5 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
124
Voted
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
15 years 5 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
133
Voted
NIPS
2004
15 years 4 months ago
Incremental Learning for Visual Tracking
Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features to handle appearance variation of...
Jongwoo Lim, David A. Ross, Ruei-Sung Lin, Ming-Hs...
133
Voted
COLING
1996
15 years 4 months ago
Learning to Recognize Names Across Languages
The development of natural language proccssing (NLP) systems that perform machine translation (MT) and information retrieval (IR) has highlighted the need for the automatic recogn...
Anthony F. Gallippi