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Object classification.
This hypothesis is tested on object classification accuracy.
Object classification in general is a challenging field.
However, the same cannot be said about object classification.
We apply MLDL for face recognition, object classification and digit classification tasks.
With this model we can demonstrate object classification and the learning of new object representations.
The detection process generally occurs in two steps: object detection and object classification.
The problem of object classification is to associate a given waveform x with an object y.
The detection process occurs in two steps: object detection and object classification.
Recently, convolutional neural network (CNN) has achieved impressive results in object classification tasks.
Applications on face recognition and object classification demonstrate that differentiated representation is very promising.
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