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We apply MLDL for face recognition, object classification and digit classification tasks.
We describe how we use the software components to perform digit classification using the MNIST dataset.
Specifically, we used the data in "Learning with Local and Global Consistency" for handwritten digit classification experiments.
Students implement the perceptron algorithm and neural network models, and apply the models to several tasks including digit classification.
We present methods for using NIDA networks on classification tasks in which there is no temporal component, in particular, the handwritten digit classification task.
Since the original data is disaggregated at the 4-digit industry code which is required for calculating the REC I weaggregate the variables "output", "wages", "employment" and "number of firms" up to the 2 digit classification of industries which is then used for calculating our "Competition" and "Diversity" indexes.
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For example, the six-digit classification distinguishes between "prepared cereals" – both roasted and non-roasted – but does not differ between those made from maize or other cereals.
This is clear from Fig. 4. Also, the year 2012 witnessed the transition from the 3-digit industry classification to 4-digit classification, resulting in some significant outliers.
Year 1995 and 1996 have been dropped from the dataset because of the non-availability of data on industries 15 30 as per the ISIC 2-digit classification.
Compustat assigns every enterprise to the industry that accounts for the highest percentage of its revenues, using the four-digit classification codes published by the U.S. Census Bureau.
At the same time, our study retains all 22 industries which constitute "manufacturing" (according to the ISIC 4-digit classification), as against only the 6 fastest growing city-industries in Glaeser et al., 1992.
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