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» Robustness Analysis of the Neural Gas Learning Algorithm
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BMCBI
2008
136views more  BMCBI 2008»
15 years 18 days ago
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
108
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ICCV
2007
IEEE
16 years 2 months ago
Robust Visual Tracking Based on Incremental Tensor Subspace Learning
Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspa...
Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhang, ...
121
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TNN
2010
227views Management» more  TNN 2010»
14 years 7 months ago
Self-Organizing MultiLayer Perceptron
Abstract--A set of Artificial Neural Network (ANN) based methods for the design of an effective system of speech recognition of numerals of Assamese language captured under varied ...
B. Gas
121
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AI
2010
Springer
14 years 7 months ago
Robustness of Classifiers to Changing Environments
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
104
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ICML
2006
IEEE
16 years 1 months ago
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PC...
Chris H. Q. Ding, Ding Zhou, Xiaofeng He, Hongyuan...