@misc{cogprints571,
           title = {How to Make a Low-Dimensional Representation Suitable for Diverse Tasks},
          author = {Nathan Intrator and Shimon Edelman},
            year = {1996},
             url = {http://cogprints.org/571/},
        abstract = {We consider training classifiers for multiple tasks as a method for improving generalization and obtaining a better low-dimensional representation. To that end, we introduce a hybrid training methodology for MLP networks; the utility of the hidden-unit representation is assessed by embedding it into a 2D space using multidimensional scaling. The proposed methodology is tested on a highly nonlinear image classification task.}
}

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