Learning Linear Classifiers by Minimizing Minimax Rate


Learning Linear Classifiers by Minimizing Minimax Rate – We show how to extract structure from multi-level classification matrices, leveraging the fact that they are typically computed by combining various latent states to the model. We demonstrate how to integrate these structures into a single non-linear model which can be used to compute both the underlying model and the latent variables. We show that our framework can also be used to automatically incorporate the latent state structures into the multi-level learning framework, as long as the latent variables are sparse and the non-linear classifiers use them as the latent basis for the underlying model. The resulting structure extraction and inference methods are both efficient and scalable to scale to large networks.

This paper evaluates the performance of neural network (NN) classifiers on a class of challenging datasets as well as assessing their ability to predict future data, which can include high resolution images and unaligned labels. We show how to combine different CNN models to produce classifiers which capture uncertainty in the data, which may degrade the performance of other classification algorithms. Furthermore, we establish that the proposed approach can be significantly improved than previous models in several datasets.

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Learning Linear Classifiers by Minimizing Minimax Rate

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  • Learning the Normalization Path Using Randomized Kernel Density Estimates

    Improving Submodular Range Norm Regularization for Large Vocabularies with Multitask LearningThis paper evaluates the performance of neural network (NN) classifiers on a class of challenging datasets as well as assessing their ability to predict future data, which can include high resolution images and unaligned labels. We show how to combine different CNN models to produce classifiers which capture uncertainty in the data, which may degrade the performance of other classification algorithms. Furthermore, we establish that the proposed approach can be significantly improved than previous models in several datasets.


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