  Increasing the scalability of machine learning to handle big volume of data
is a challenging task. The scale up approach has some limitations. In this
paper, we proposed a scale out approach for CNN-ELM based on MapReduce on
classifier level. Map process is the CNN-ELM training for certain partition of
data. It involves many CNN-ELM models that can be trained asynchronously.
Reduce process is the averaging of all CNN-ELM weights as final training
result. This approach can save a lot of training time than single CNN-ELM
models trained alone. This approach also increased the scalability of machine
learning by combining scale out and scale up approaches. We verified our method
in extended MNIST data set and not-MNIST data set experiment. However, it has
some drawbacks by additional iteration learning parameters that need to be
carefully taken and training data distribution that need to be carefully
selected. Further researches to use more complex image data set are required.
