A Support Vector Machine (SVM) classifier employs a kernel ƒ(xi,y)∝Πd=1DF(xi,d,yd) or ƒ(xi,y)∝σd=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and vector y is a vector being classified, e.g. representing a message communicated on a Controller Area Network (CAN) bus of a vehicle. The SVM classifier comprises a microprocessor or microcontroller programmed to perform classification using the SVM classifier with at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table. For those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of values. In an embedded system, the microprocessor or microcontroller performs an embedded system function such as engine control, brake control, entertainment system operation, or so forth, or an embedded system function for a vehicle, ocean buoy, or satellite.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A Support Vector Machine (SVM) classifier employing a kernel ƒ(xi,y)∝Πd=1DF(xi,d,yd) or a kernel ƒ(xi,y)∝Σd=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and vector y is a vector being classified, the SVM classifier comprising a microprocessor or microcontroller programmed to perform classification using the SVM classifier with at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table. 2. The SVM classifier of claim 1 wherein y represents a message communicated on a Controller Area Network (CAN) bus of a vehicle. 3. The SVM classifier of claim 1 wherein the kernel ƒ(xi,y) is a Radial Basis Function (RBF) kernel. 4. The SVM classifier of claim 1 wherein for those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of values. 5. The SVM classifier of claim 1 wherein for those terms F(xi,d,yd) of the kernel ƒ(xi,y) determined using a look-up table, the dimension yd can only assume values defined by a finite set of integers. 6. The SVM classifier of claim 1 wherein the microprocessor or microcontroller programmed to perform classification using the SVM classifier comprises the microprocessor or microcontroller of an embedded system. 7. An embedded system comprising the microprocessor or microcontroller of claim 1. 8. The embedded system of claim 7 wherein the microprocessor or microcontroller is further programmed to perform an embedded system function such as engine control, brake control, entertainment system operation, or so forth. 9. The embedded system of claim 7 wherein the microprocessor or microcontroller is further programmed to perform an embedded system function for a vehicle, ocean buoy, or satellite. 10. A Support Vector Machine (SVM) classifier training system comprising a computer programmed to: train an SVM classifier employing a kernel ƒ(xi, y)∝Πd=1DF(xi,d,yd) or a kernel ƒ(xi,y)∝Σd=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and of data vectors y; and generate a look-up table containing the possible values for at least one term F(xi,d,yd) of the kernel ƒ(xi,y). 11. The SVM classifier training system of claim 10 wherein the computer comprises a cloud-based computing resource, a parallel processor system, or a cluster computer. 12. The SVM classifier training system of claim 10 wherein the kernel ƒ(xi,y) is a Radial Basis Function (RBF) kernel. 13. The SVM classifier training system of claim 10 wherein, for the at least one term F(xi,d,yd) of the kernel ƒ(xi,y) for which a look-up table is generated, the dimension yd can only assume values defined by a finite set of values. 14. The SVM classifier training system of claim 10 wherein, for the at least one term F(xi,d,yd) of the kernel ƒ(xi, y) for which a look-up table is generated, the dimension yd can only assume values defined by a finite set of integers. 15. A non-transitory storage medium storing instructions readable and executable by at least one computer and/or embedded system to perform a classification method comprising: applying a Support Vector Machine (SVM) classifier employing a kernel ƒ(xi,y)∝Πd=1DF(xi,d,yd) or a kernel ƒ(xi,y)∝σd=1DF(xi,d,yd) to classify a vector y, where D is the dimensionality of Support Vectors xi; wherein the applying includes determining at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) using a look-up table. 16. A method comprising: generating a Support Vector Machine (SVM) classifier by operations including: (1) training an SVM classifier employing a kernel ƒ(xi,y)∝Πd=1DF(xi,d,yd) or a kernel ƒ(xi,y)∝Σd=1DF(xi,d,yd) where D is the dimensionality of Support Vectors xi and of data vectors y; and (2) generating a look-up table containing the possible values for at least one term F(xi,d,yd) of the kernel ƒ(xi,y) which term F(xi,d,yd) has a finite number of possible values. 17. The method of claim 16 further comprising: (3) applying the trained SVM classifier to classify a vector y, wherein the applying includes determining at least some terms F(xi,d,yd) of the kernel ƒ(xi,y) using the generated look-up table. 18. The method of claim 17 wherein operations (1) and (2) are performed by a computer and operation (3) is performed by a microprocessor or microcontroller of an embedded system other than the computer. 19. The method of claim 17 wherein operations (1) and (2) are performed by a computer and operation (3) is performed by an embedded system of a vehicle.