Method and electronic device for classifying an input

An electronic device comprising:

• a memory configured to store program instruction;
• and
• a processor configured to execute the program instruction,
• wherein the memory the processor and the program instructions are configured to,
• obtain an input to be classified,
• train a neural network in a feed-forward manner based on a plurality of sample inputs and sample classifications, each of the sample classifications associated with one of the plurality of sample inputs, the training including, for each of a plurality of layers in the neural network, selecting values for a weight matrix, parameters for a plurality of activation unit functions, and a plurality of linear filter parameters, and
• classify the input using the trained neural network based on the weight matrix, the parameters for the plurality of activation unit functions, and the plurality of linear filter parameters for each of the plurality of layers.

A non-transitory computer readable medium including program instructions which, when executed by a processor, cause the processor to:

• obtain an input to be classified;
• train a neural network in a feed-forward manner based on a plurality of sample inputs and sample classifications, each of the sample classifications associated with one of the plurality of sample inputs, the training including, for each of a plurality of layers in the neural network, selecting values for a weight matrix, parameters for a plurality of activation unit functions, and a plurality of linear filter parameters;
• and
• classify the input using the trained neural network based on the weight matrix, the parameters for the plurality of activation unit functions, and the plurality of linear filter parameters for each of the plurality of layers.