Disclosed is a learning method based on a stochastic-based deep learning model having a non-consecutive stochastic neural. The learning method includes configuring a non-consecutive stochastic feedforward neural network (NCSFNN) having non-consecutive stochastic neuron as a leaning model including a plurality of hidden layers; and allowing the NCSFNN to learn.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning method comprising: configuring a non-consecutive stochastic feedforward neural network (NCSFNN) having non-consecutive stochastic neuron as a leaning model including a plurality of hidden layers; and allowing the NCSFNN to learn. 2. The learning method of claim 1, wherein the configuring of the NCSFNN comprises configuring a last layer of the NCSFNN as a non-stochastic neuron. 3. The learning method of claim 1, wherein the configuring of the NCSFNN comprises configuring the NCSFNN by replacing at least one of a deep neural network (DNN) with a stochastic layer. 4. The learning method of claim 1, wherein the configuring of the NCSFNN comprises configuring at least one among the hidden layers with a stochastic layer and configuring a last layer with a non-stochastic layer. 5. The learning method of claim 4, wherein the configuring of the NCSFNN comprises configuring a layer connected to an output of the stochastic layer with a deterministic layer. 6. The learning method of claim 4, wherein the stochastic layer is defined as a binary random vector having marginal distribution expressed as follows: P  ( h 1 ; x ) = ∏ i = 1 N 1  P  ( h i 1 ; x )   with P  ( h i 1 = 1 ; x ) = g  ( α 1  f  ( W i 1  x + b i 1 ) ) wherein x is data to be learned, N1 is a number of hidden units of the stochastic layer, Wi1 is an i-th weight matrix of the stochastic layer, bi1 is an i-th bias of the stochastic layer, f:→+ is a non-negative activation function, and g(x)=min(max(x,0), 1), α1>0 is a parameter of the stochastic layer. 7. The learning method of claim 4, wherein the non-stochastic layer is defined as a deterministic vector expressed as follows: h2(x)=[f(α2(P(h1;x) [s(Wj2h1+bj2)]−s(0))):∀j ∈ N2] wherein x is data to be learned, N2 is a number of hidden units of the non-stochastic layer, Wj2 is an j-th weight matrix of the non-stochastic layer, bj2 is an j-th bias of the non-stochastic layer, f:→+ is a non-negative activation function, α2>0 is a parameter of the non-stochastic layer, and s:→ is a non-linear activation function. 8. The learning method of claim 1, wherein the allowing of the NCSFNN to learn is performed based on a knowledge transfer and gradient estimation 9. The learning method of claim 3, wherein the allowing of the NCSFNN to learn comprises setting a parameter of the NCSFNN through linear transformation by using a parameter of the DNN. 10. The learning method of claim 3, wherein the allowing of the NCSFNN to learn comprises allowing the NCSFNN to learn in a two-stage learning scheme of allowing the DNN to learn and allowing the NCSFNN to learn after a parameter of the NCSFNN is set by using a parameter of the DNN learned. 11. The learning method of claim 1, wherein the NCSFNN is used for supervised learning for recognizing a thing or a voice. 12. A learning method comprising: configuring a non-consecutive stochastic feedforward neural network (NCSFNN) by replacing at least one non-consecutive layer with a stochastic layer in a deep neural network (DNN) including a plurality of hidden layers; and allowing the NCSFNN to learn based on a knowledge transfer and gradient estimation. 13. The learning method of claim 12, wherein the configuring of the NCSFNN comprises configuring a last layer among the hidden layers with a non-stochastic layer. 14. The learning method of claim 13, wherein the configuring of the NCSFNN comprises configuring a layer connected to an output of the stochastic layer with a deterministic layer. 15. The learning method of claim 12, wherein the NCSFNN is used for supervised learning for recognizing a thing or a voice. 16. A learning method comprising: configuring a non-consecutive stochastic feedforward neural network (NCSFNN) by replacing at least one non-consecutive layer with a stochastic layer in a deep neural network including a plurality of hidden layers; and allowing the NCSFNN to learn in a two-stage learning scheme of allowing the DNN to learn and allowing the NCSFNN to learn after a parameter of the NCSFNN is set by using a parameter of the DNN learned. 17. The learning method of claim 16, wherein the configuring of the NCSFNN comprises configuring a last layer among the hidden layers with a non-stochastic layer. 18. The learning method of claim 17, wherein the configuring of the NCSFNN comprises configuring a layer connected to an output of the stochastic layer with a deterministic layer. 19. The learning method of claim 16, wherein the NCSFNN is used for supervised learning for recognizing a thing or a voice. 20. A learning system implemented by a computer, the learning system comprising at least one processor implemented to execute an instruction readable by the computer, wherein the at least one processor configures a non-consecutive stochastic feedforward neural network (NCSFNN) by replacing at least one non-consecutive layer with a stochastic layer in a deep neural network (DNN) including a plurality of hidden layers. 21. The learning system of claim 20, wherein the at least one processor configures a last layer among the hidden layers as a non-stochastic layer to configure the NCSFNN. 22. The learning system of claim 20, wherein the at least one processor allows the NCSFNN to learn based on a knowledge transfer and gradient estimation, and wherein the at least one processor allows the NCSFNN to learn a two-stage learning scheme of allowing the DNN to learn and allowing the NCSFNN to learn after a parameter of the NCSFNN is set by using a parameter of the DNN learned. 23. The learning system of claim 20, wherein the at least one processor uses the NCSFNN for supervised learning for recognizing a thing or a voice.