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A new biologically motivated framework for robust object recognition
In this paper, we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety ofobject categories while being capable of learning from only a fewtraining examples. ...
Selecting Relevant Genes with a Spectral Approach
Array technologies have made it possible to record simultaneouslythe expression pattern of thousands of genes. A fundamental problemin the analysis of gene expression data is the identification ofhighly relevant genes that ...
Combining Variable Selection with Dimensionality Reduction
This paper bridges the gap between variable selection methods (e.g., Pearson coefficients, KS test) and dimensionality reductionalgorithms (e.g., PCA, LDA). Variable selection algorithms encounter difficulties dealing with ...
Regularization Through Feature Knock Out
In this paper, we present and analyze a novel regularization technique based on enhancing our dataset with corrupted copies of the original data. The motivation is that since the learning algorithm lacks information about ...