Hi researchers! I am a learner of statistics learing and machine learning. After applying the Convolutional neural networks into image recognition and text mining, I think this method is powerful in classification. So I want to apply them into statistics field and want to know the advantages and disadvantages of CNNs. Can I view it as a specia kind of "regression", which is blessed with flexible model form and interaction form？ What is the disadvantage of it? Thank you very much!
The main adventage is their accuracy in image recognition problems.
They have some disadventages:
-High computational cost.
- If you don't have a good GPU they are quite slow to train (for complex tasks).
-They use to need a lot of training data.
hi please visit the below link
http://www.cs.toronto.edu/~hinton/
http://deeplearning.net/reading-list/
Conference Paper Twitter Sentiment Analysis with Deep Convolutional Neural Networks
One of the weaknesses was pointed out by Severyn et al. (link above).
It was the dependence of CNNs on the initial parameter tuning (for a good point) to avoid local optima. Thus, a weakness of CNNs is the considerable amount of work they require to initialize according to the problem at hand. This would require some expert knowledge in the domain.
hyper-parameter tuning is non-trivial, need big dataset for proper training, still black box, comparatively slow
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