A computing device determines a kernel parameter value for a support vector data description for outlier identification. A first candidate optimal kernel parameter value is computed by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value. The first objective function is defined for a SVDD model using observation vectors to define support vectors. A number of the observation vectors is a predefined sample size. The predefined sample size is incremented by adding a sample size increment. A next candidate optimal kernel parameter value is computed with an incremented number of vectors until a computed difference value is less than or equal to a predefined convergence value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A non-transitory computer-readable medium having stored thereon computer-readable instructions that when executed by a computing device cause the computing device to: compute a first candidate optimal kernel parameter value by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value, wherein the first objective function is defined for a support vector data description (SVDD) model using a first set of observation vectors to define a first set of support vectors, wherein a number of the first set of observation vectors is a predefined sample size, wherein the first set of support vectors define a first data description for a training dataset that includes the first set of observation vectors; (a) increment the predefined sample size by adding a predefined sample size increment to the predefined sample size; (b) compute a second candidate optimal kernel parameter value by computing a second optimal value of a second objective function that includes the kernel function for each of the plurality of kernel parameter values, wherein the second objective function is defined for the SVDD model using a second set of observation vectors to define a second set of support vectors, wherein a number of the second set of observation vectors is the incremented, predefined sample size, wherein the second set of support vectors define a second data description for the training dataset that includes the second set of observation vectors; (c) compute a difference value between the computed second candidate optimal kernel parameter value and the computed first candidate optimal kernel parameter value; repeat (a)-(c) with the computed first candidate optimal kernel parameter value as the computed second candidate optimal kernel parameter value until the computed difference value is less than or equal to a predefined convergence value; and when the computed difference value is less than or equal to the predefined convergence value, output the computed second candidate optimal kernel parameter value for identifying an outlier in a scoring dataset. 2. The non-transitory computer-readable medium of claim 1, wherein the kernel function is a Gaussian kernel function, and each of the plurality of kernel parameter values is a Gaussian bandwidth parameter. 3. The non-transitory computer-readable medium of claim 1, wherein computing the first candidate optimal kernel parameter value comprises computer-readable instructions that when executed by a computing device cause the computing device to: for each kernel parameter value of the plurality of kernel parameter values, compute a third optimal value of the first objective function defined for the SVDD model using the kernel function, the first set of observation vectors, and a respective value for the kernel parameter to define a first plurality of sets of support vectors, where each set of support vectors of the first plurality of sets of support vectors defines a first boundary for the first set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, store the computed third optimal value in association with the respective value for the kernel parameter; compute first difference function values between the computed third optimal values associated with successive values for the kernel parameter; and fit a first curve to the computed first difference function values; wherein the first candidate optimal kernel parameter value is computed as a first value for the kernel parameter where the fit first curve has a first extremum value as the kernel parameter value increases. 4. The non-transitory computer-readable medium of claim 3, wherein the first curve is fit to the computed first difference function values using a penalized B-spline curve fit function. 5. The non-transitory computer-readable medium of claim 3, wherein computing the second candidate optimal kernel parameter value comprises computer-readable instructions that when executed by a computing device cause the computing device to: for each kernel parameter value of the plurality of kernel parameter values, compute a fourth optimal value of the second objective function defined for the SVDD model using the kernel function, the second set of observation vectors, and the respective value for the kernel parameter to define a second plurality of sets of support vectors, where each set of support vectors of the second plurality of sets of support vectors defines a second boundary for the second set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, store the computed fourth optimal value in association with the respective value for the kernel parameter; compute second difference function values between the computed fourth optimal values associated with successive values for the kernel parameter; and fit a second curve to the computed second difference function values; wherein the second candidate optimal kernel parameter value is computed as a second value for the kernel parameter where the fit second curve has a second extremum value as the kernel parameter value increases. 6. The non-transitory computer-readable medium of claim 5, wherein the second curve is fit to the computed second difference function values using a penalized B-spline curve fit function. 7. The non-transitory computer-readable medium of claim 1, wherein the first objective function defined for the SVDD model is max(Σi=1nαiK(xi,xi)−Σi=1nΣj=1nαiαjK (xi,xj)), subject to Σi=1nαi=1 and 0≦αi≦C, ∀i=1, . . . n, where K(xi,xj) is the kernel function, n is the predefined sample size, C=1/nf where f is an expected outlier fraction, xi and xj are the first set of observation vectors, and αi and αj are Lagrange constants. 8. The non-transitory computer-readable medium of claim 7, wherein the xi that have 0<αi≦C are the defined first set of support vectors. 9. The non-transitory computer-readable medium of claim 7, wherein the second objective function defined for the SVDD model is max(Σi=1nincα2iK(x2i,x2i)−Σi=1nincΣj=1nincα2iα2jK(x2i,x2j)), subject to Σi=1nincα2i=1 and 0≦α2i≦Cinc, ∀i=1, . . . , ninc, ninc is the incremented, predefined sample size, Cinc=1/nincf, x2i and x2j are the second set of observation vectors, and α2i and α2j are the Lagrange constants for the second objective function defined for the SVDD model. 10. The non-transitory computer-readable medium of claim 9, wherein the x2i that have 0<α2i≦Cinc are the defined second set of support vectors. 11. The non-transitory computer-readable medium of claim 10, wherein, when the computed difference value is less than or equal to the predefined convergence value, the computer-readable instructions further cause the computing device to output the Lagrange constant αi for each of the defined second set of support vectors and the defined second set of support vectors that are associated with the computed second optimal kernel parameter value. 12. The non-transitory computer-readable medium of claim 11, wherein, when the computed difference value is less than or equal to the predefined convergence value, the computer-readable instructions further cause the computing device to compute a threshold using the defined second set of support vectors that are associated with the computed second optimal kernel parameter value. 13. The non-transitory computer-readable medium of claim 12, wherein the threshold is computed using R2=K(xk,xk)−2 Σi=1Nα2iK(x2i,xk)+Σi=1NΣj=1Nα2iα2jK(x2i,x2j), where xk is any support vector of the defined second set of support vectors, and N is a number of support vectors included in the defined second set of support vectors. 14. The non-transitory computer-readable medium of claim 12, wherein, when the computed difference value is less than or equal to the predefined convergence value, the computer-readable instructions further cause the computing device to output the computed threshold for identifying the outlier. 15. The non-transitory computer-readable medium of claim 13, wherein, after outputting the defined second set of support vectors, the computer-readable instructions further cause the computing device to: read an observation vector from a second dataset; compute a distance value using the defined second set of support vectors, the Lagrange constants, and the read observation vector; and when the computed distance value is greater than the computed threshold, identify the read observation vector as an outlier. 16. The non-transitory computer-readable medium of claim 15, wherein the distance value is computed using dist2(z)=K(z,z)−2Σi=1Nα2iK(x2i,z)+Σi=1NΣj=1Nα2iα2jK(x2i,x2j), where z is the read observation vector. 17. The non-transitory computer-readable medium of claim 15, wherein when the computed distance value is not greater than the computed threshold, the read observation vector is not identified as an outlier. 18. The non-transitory computer-readable medium of claim 1, wherein each observation vector of the first set of observation vectors and of the second set of observation vectors includes a plurality of values, wherein each value of the plurality of values is associated with a variable to define a plurality of variables, wherein each variable of the plurality of variables describes a characteristic of a physical object. 19. The non-transitory computer-readable medium of claim 1, wherein each observation vector of the first set of observation vectors and of the second set of observation vectors is randomly selected from the training dataset. 20. The non-transitory computer-readable medium of claim 1, wherein the predefined convergence value is computed as a function of the computed first candidate optimal kernel parameter value. 21. A computing device comprising: a processor; and a non-transitory computer-readable medium operably coupled to the processor, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the processor, cause the computing device to compute a first candidate optimal kernel parameter value by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value, wherein the first objective function is defined for a support vector data description (SVDD) model using a first set of observation vectors to define a first set of support vectors, wherein a number of the first set of observation vectors is a predefined sample size, wherein the first set of support vectors define a first data description for a training dataset that includes the first set of observation vectors; (a) increment the predefined sample size by adding a predefined sample size increment to the predefined sample size; (b) compute a second candidate optimal kernel parameter value by computing a second optimal value of a second objective function that includes the kernel function for each of the plurality of kernel parameter values, wherein the second objective function is defined for the SVDD model using a second set of observation vectors to define a second set of support vectors, wherein a number of the second set of observation vectors is the incremented, predefined sample size, wherein the second set of support vectors define a second data description for the training dataset that includes the second set of observation vectors; (c) compute a difference value between the computed second candidate optimal kernel parameter value and the computed first candidate optimal kernel parameter value; repeat (a)-(c) with the computed first candidate optimal kernel parameter value as the computed second candidate optimal kernel parameter value until the computed difference value is less than or equal to a predefined convergence value; and when the computed difference value is less than or equal to the predefined convergence value, output the computed second candidate optimal kernel parameter value for identifying an outlier in a scoring dataset. 22. The computing device of claim 21, wherein computing the first candidate optimal kernel parameter value comprises computer-readable instructions that when executed by a computing device cause the computing device to: for each kernel parameter value of the plurality of kernel parameter values, compute a third optimal value of the first objective function defined for the SVDD model using the kernel function, the first set of observation vectors, and a respective value for the kernel parameter to define a first plurality of sets of support vectors, where each set of support vectors of the first plurality of sets of support vectors defines a first boundary for the first set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, store the computed third optimal value in association with the respective value for the kernel parameter; compute first difference function values between the computed third optimal values associated with successive values for the kernel parameter; and fit a first curve to the computed first difference function values; wherein the first candidate optimal kernel parameter value is computed as a first value for the kernel parameter where the fit first curve has a first extremum value as the kernel parameter value increases. 23. The computing device of claim 22, wherein the first curve is fit to the computed first difference function values using a penalized B-spline curve fit function. 24. The computing device of claim 22, wherein computing the second candidate optimal kernel parameter value comprises computer-readable instructions that when executed by a computing device cause the computing device to: for each kernel parameter value of the plurality of kernel parameter values, compute a fourth optimal value of the second objective function defined for the SVDD model using the kernel function, the second set of observation vectors, and the respective value for the kernel parameter to define a second plurality of sets of support vectors, where each set of support vectors of the second plurality of sets of support vectors defines a second boundary for the second set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, store the computed fourth optimal value in association with the respective value for the kernel parameter; compute second difference function values between the computed fourth optimal values associated with successive values for the kernel parameter; and fit a second curve to the computed second difference function values; wherein the second candidate optimal kernel parameter value is computed as a second value for the kernel parameter where the fit second curve has a second extremum value as the kernel parameter value increases. 25. The computing device of claim 21, wherein each observation vector of the first set of observation vectors and of the second set of observation vectors is randomly selected from the training dataset. 26. A method of determining a kernel parameter value for a support vector data description for outlier identification, the method comprising: computing, by a computing device, a first candidate optimal kernel parameter value by computing a first optimal value of a first objective function that includes a kernel function for each of a plurality of kernel parameter values from a starting kernel parameter value to an ending kernel parameter value using an incremental kernel parameter value, wherein the first objective function is defined for a support vector data description (SVDD) model using a first set of observation vectors to define a first set of support vectors, wherein a number of the first set of observation vectors is a predefined sample size, wherein the first set of support vectors define a first data description for a training dataset that includes the first set of observation vectors; (a) incrementing, by the computing device, the predefined sample size by adding a predefined sample size increment to the predefined sample size; (b) computing, by the computing device, a second candidate optimal kernel parameter value by computing a second optimal value of a second objective function that includes the kernel function for each of the plurality of kernel parameter values, wherein the second objective function is defined for the SVDD model using a second set of observation vectors to define a second set of support vectors, wherein a number of the second set of observation vectors is the incremented, predefined sample size, wherein the second set of support vectors define a second data description for the training dataset that includes the second set of observation vectors; (c) computing, by the computing device, a difference value between the computed second candidate optimal kernel parameter value and the computed first candidate optimal kernel parameter value; repeating, by the computing device, (a)-(c) with the computed first candidate optimal kernel parameter value as the computed second candidate optimal kernel parameter value until the computed difference value is less than or equal to a predefined convergence value; and when the computed difference value is less than or equal to the predefined convergence value, outputting, by the computing device, the computed second candidate optimal kernel parameter value for identifying an outlier in a scoring dataset. 27. The method of claim 26, wherein computing the first candidate optimal kernel parameter value comprises: for each kernel parameter value of the plurality of kernel parameter values, computing, by the computing device, a third optimal value of the first objective function defined for the SVDD model using the kernel function, the first set of observation vectors, and a respective value for the kernel parameter to define a first plurality of sets of support vectors, where each set of support vectors of the first plurality of sets of support vectors defines a first boundary for the first set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, storing, by the computing device, the computed third optimal value in association with the respective value for the kernel parameter; computing, by the computing device, first difference function values between the computed third optimal values associated with successive values for the kernel parameter; and fitting, by the computing device, a first curve to the computed first difference function values; wherein the first candidate optimal kernel parameter value is computed as a first value for the kernel parameter where the fit first curve has a first extremum value as the kernel parameter value increases. 28. The method of claim 27, wherein the first curve is fit to the computed first difference function values using a penalized B-spline curve fit function. 29. The method of claim 27, wherein computing the second candidate optimal kernel parameter value comprises: for each kernel parameter value of the plurality of kernel parameter values, computing, by the computing device, a fourth optimal value of the second objective function defined for the SVDD model using the kernel function, the second set of observation vectors, and the respective value for the kernel parameter to define a second plurality of sets of support vectors, where each set of support vectors of the second plurality of sets of support vectors defines a second boundary for the second set of observation vectors in association with the respective value for the kernel parameter; for each kernel parameter value of the plurality of kernel parameter values, storing, by the computing device, the computed fourth optimal value in association with the respective value for the kernel parameter; computing, by the computing device, second difference function values between the computed fourth optimal values associated with successive values for the kernel parameter; and fitting, by the computing device, a second curve to the computed second difference function values; wherein the second candidate optimal kernel parameter value is computed as a second value for the kernel parameter where the fit second curve has a second extremum value as the kernel parameter value increases. 30. The method of claim 26, wherein each observation vector of the first set of observation vectors and of the second set of observation vectors is randomly selected from the training dataset.