Techniques are disclosed for improving classification performance in supervised learning. In accordance with some embodiments, a multiclass support vector machine (SVM) having three or more classes may be converted to a plurality of binary problems that then may be reduced via one or more reduced-set methods. The resultant reduced-set (RS) vectors may be combined together in one or more joint lists, along with the original support vectors (SVs) of the different binary classes. Each binary problem may be re-trained using the joint list(s) by applying a reduction factor (RF) parameter to reduce the total quantity of RS vectors. In re-training, different kernel methods can be combined, in accordance with some embodiments. Reduction may be performed until desired classification performance is achieved. The disclosed techniques can be used, for example, to improve classification speed, accuracy, class prioritization, or a combination thereof, in the SVM training phase, in accordance with some embodiments.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system comprising: at least one memory; and at least one processor configured to access the at least one memory and to: convert a multiclass problem of a support vector machine (SVM) to at least one binary problem; reduce the at least one binary problem via at least one reduced-set method; generate at least one joint list comprising: at least one reduced-set (RS) vector resulting from reduction of the at least one binary problem; and at least one original support vector (SV) of the at least one binary problem; and re-train the at least one binary problem with the at least one joint list. 2. The computing system of claim 1, wherein the at least one reduced-set method comprises at least one of Burges reduced-set vector method (BRSM) and a Gaussian reduced-set vector method (GRSM). 3. The computing system of claim 1, wherein the at least one reduced-set method applies at least one of a polynomial function and a Gaussian radial basis function (RBF). 4. The computing system of claim 1, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a single joint list generated based on a single kernel function of the plurality of reduced-set methods, wherein the kernel function is at least one of a polynomial function and a Gaussian function. 5. The computing system of claim 1, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a plurality of joint lists including at least: a first joint list generated based on a first kernel function of the plurality of reduced-set methods; and a second joint list generated based on a second kernel function of the plurality of reduced-set methods. 6. The computing system of claim 1, wherein in re-training the at least one binary problem with the at least one joint list, the at least one processor is configured to apply at least one reduction factor (RF) parameter to reduce a quantity of the at least one RS vector. 7. The computing system of claim 6, wherein: the at least one joint list is a plurality of joint lists; and the at least one RF parameter gives priority to a given joint list over another. 8. The computing system of claim 1, wherein in re-training the at least one binary problem with the at least one joint list, the at least one processor is further configured to reduce a quantity of the at least one original SV based on a weighting factor. 9. A non-transitory computer program product encoded with instructions that, when executed by one or more processors, causes a process to be carried out, the process comprising: converting a multiclass problem of a support vector machine (SVM) to at least one binary problem; reducing the at least one binary problem via at least one reduced-set method; generating at least one joint list comprising: at least one reduced-set (RS) vector resulting from reduction of the at least one binary problem; and at least one original support vector (SV) of the at least one binary problem; and re-training the at least one binary problem with the at least one joint list. 10. The non-transitory computer program product of claim 9, wherein the at least one reduced-set method comprises at least one of Burges reduced-set vector method (BRSM) and a Gaussian reduced-set vector method (GRSM). 11. The non-transitory computer program product of claim 9, wherein the at least one reduced-set method applies at least one of a polynomial function and a Gaussian radial basis function (RBF). 12. The non-transitory computer program product of claim 9, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a single joint list generated based on a single kernel function of the plurality of reduced-set methods, wherein the kernel function is at least one of a polynomial function and a Gaussian function. 13. The non-transitory computer program product of claim 9, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a plurality of joint lists including at least: a first joint list generated based on a first kernel function of the plurality of reduced-set methods; and a second joint list generated based on a second kernel function of the plurality of reduced-set methods. 14. The non-transitory computer program product of claim 9, wherein re-training the at least one binary problem with the at least one joint list comprises: applying at least one reduction factor (RF) parameter to reduce a quantity of the at least one RS vector. 15. The non-transitory computer program product of claim 14, wherein: the at least one joint list is a plurality of joint lists; and the at least one RF parameter gives priority to a given joint list over another. 16. The non-transitory computer program product of claim 9, wherein re-training the at least one binary problem with the at least one joint list comprises: reducing a quantity of the at least one original SV based on a weighting factor. 17. A method of improving classification performance of a multiclass support vector machine (SVM), the method comprising: converting a multiclass problem of a support vector machine (SVM) to at least one binary problem; reducing the at least one binary problem via at least one reduced-set method; generating at least one joint list comprising: at least one reduced-set (RS) vector resulting from reduction of the at least one binary problem; and at least one original support vector (SV) of the at least one binary problem; and re-training the at least one binary problem with the at least one joint list. 18. The method of claim 17, wherein the at least one reduced-set method comprises at least one of Burges reduced-set vector method (BRSM) and a Gaussian reduced-set vector method (GRSM). 19. The method of claim 17, wherein the at least one reduced-set method applies at least one of a polynomial function and a Gaussian radial basis function (RBF). 20. The method of claim 17, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a single joint list generated based on a single kernel function of the plurality of reduced-set methods, wherein the kernel function is at least one of a polynomial function and a Gaussian function. 21. The method of claim 17, wherein: the at least one reduced-set method is a plurality of reduced-set methods; and the at least one joint list is a plurality of joint lists including at least: a first joint list generated based on a first kernel function of the plurality of reduced-set methods; and a second joint list generated based on a second kernel function of the plurality of reduced-set methods. 22. The method of claim 17, wherein re-training the at least one binary problem with the at least one joint list comprises: applying at least one reduction factor (RF) parameter to reduce a quantity of the at least one RS vector. 23. The method of claim 22, wherein: the at least one joint list is a plurality of joint lists; and the at least one RF parameter gives priority to a given joint list over another. 24. The method of claim 17, wherein re-training the at least one binary problem with the at least one joint list comprises: reducing a quantity of the at least one original SV based on a weighting factor.