HYBRID SPIKING NEURAL NETWORK AND SUPPORT VECTOR MACHINE CLASSIFIER
System and techniques for a spiking neural network and support vector machine hybrid classifier are described herein. A first set of sensor data may be obtained, e.g., from a corpus of sample sensor data. A feature set is extracted from the sensor data using a spiking neural network (SNN). A support vector machine (SVM) may then be created for the sensor data using the feature set. The SVM may then be used to classify a second set of sensor data.
1 . A system for a hybrid spiking neural network and support vector machine classifier, the system comprising:
an interface to obtain a first set of sensor data;
a memory to store executable computer program instructions; and
processing circuitry configured by the computer program instructions to:
extract one or more feature sets from the sensor data using a spiking neural network (SNN);
create a support vector machine (SVM) for the sensor data using the feature sets; and
classify a second set of sensor data using the SVM.
2 . The system of claim 1 , wherein the first set of sensor data is encoded as a frequency of spikes.
3 . The system of claim 1 , wherein the SVM is a reduced set vector SVM that uses eigenvectors, derived from support vectors, in place of the support vectors.
4 . The system of claim 3 , wherein the SVM is a multiclass SVM.
5 . The system of claim 4 , wherein, to create the SVM, the processing circuitry creates SVM solutions for binary classifications of a set of possible classifications, a binary classification separating input into one of two classes.
6 . The system of claim 5 , wherein, to create the SVM, the processing circuitry:
combines reduced set vectors for all SVM solutions for binary classifications into a single joint list by pruning a plurality of selected vectors; and
retrains all binary SVM solutions using the joint list.
7 . The system of claim 6 , wherein original support vectors for each SVM solution for binary classifications are also included in the joint list.
8 . The system of claim 6 , wherein one of several kernels is used in the retraining.
9 . A method for a hybrid spiking neural network and support vector machine classifier, the method comprising:
obtaining a first set of sensor data;
extracting one or more feature sets from the sensor data using a spiking neural network (SNN);
creating a support vector machine (SVM) for the sensor data using the feature sets; and
classifying a second set of sensor data using the SVM.
10 . The method of claim 9 , wherein the first set of sensor data is encoded as a frequency of spikes.
11 . The method of claim 9 , wherein the SVM is a reduced set vector SVM that uses eigenvectors, derived from support vectors, in place of the support vectors.
12 . The method of claim 11 , wherein the SVM is a multiclass SVM.
13 . The method of claim 12 , wherein creating the SVM includes creating SVM solutions for binary classifications of a set of possible classifications, a binary classification separating input into one of two classes.
14 . The method of claim 13 , wherein creating the SVM includes:
combining reduced set vectors for all SVM solutions for binary classifications into a single joint list by pruning a plurality of selected vectors; and
retraining all binary SVM solutions using the joint list.
15 . The system of claim 6 , wherein original support vectors for each SVM solution for binary classifications are also included in the joint list.
16 . The system of claim 6 , wherein one of several kernels is used in the retraining.
17 . At least one computer readable medium including executable computer program instructions for a hybrid spiking neural network and support vector machine classifier, the computer program instructions, when executed by a machine, cause the machine to perform operations comprising:
obtaining a first set of sensor data;
extracting one or more feature sets from the sensor data using a spiking neural network (SNN);
creating a support vector machine (SVM) for the sensor data using the feature sets; and
classifying a second set of sensor data using the SVM.
18 . The computer readable medium of claim 17 , wherein the first set of sensor data is encoded as a frequency of spikes.
19 . The computer readable medium of claim 17 , wherein the SVM is a reduced set vector SVM that uses eigenvectors, derived from support vectors, in place of the support vectors.
20 . The computer readable medium of claim 19 , wherein the SVM is a multiclass SVM.
21 . The computer readable medium of claim 20 , wherein creating the SVM includes creating SVM solutions for binary classifications of a set of possible classifications, a binary classification separating input into one of two classes.
22 . The computer readable medium of claim 21 , wherein creating the SVM includes:
combining reduced set vectors for all SVM solutions for binary classifications into a single joint list by pruning a plurality of selected vectors; and
retraining all binary SVM solutions using the joint list.
23 . The computer readable medium of claim 22 , wherein original support vectors for each SVM solution for binary classifications are also included in the joint list.
24 . The computer readable medium of claim 22 , wherein one of several kernels is used in the retraining.