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» Weighted and Robust Incremental Method for Subspace Learning
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IJCNN
2007
IEEE
14 years 1 months ago
Random Feature Subset Selection for Analysis of Data with Missing Features
Abstract - We discuss an ensemble-of-classifiers based algorithm for the missing feature problem. The proposed approach is inspired in part by the random subspace method, and in pa...
Joseph DePasquale, Robi Polikar
ICDM
2010
IEEE
167views Data Mining» more  ICDM 2010»
13 years 5 months ago
Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method
On large datasets, the popular training approach has been stochastic gradient descent (SGD). This paper proposes a modification of SGD, called averaged SGD with feedback (ASF), tha...
Xu Sun, Hisashi Kashima, Takuya Matsuzaki, Naonori...
CVPR
2004
IEEE
14 years 9 months ago
Local Smoothing for Manifold Learning
We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. The methods can be u...
Jin Hyeong Park, Zhenyue Zhang, Hongyuan Zha, Rang...
ML
2008
ACM
13 years 7 months ago
Incremental exemplar learning schemes for classification on embedded devices
Although memory-based classifiers offer robust classification performance, their widespread usage on embedded devices is hindered due to the device's limited memory resources...
Ankur Jain, Daniel Nikovski
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
14 years 7 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen