Some embodiments include a method of training an electronic transaction event classifier model to rank electronic transaction events. The method can include: receiving a training set of observable events corresponding to a set of quantifiable feature vectors, a set of known outcome labels, and a set of known cost values; setting initial parameters of a kernel function of a support vector machine; and iterating through potential weight vectors of the kernel function until the kernel function, as configured by a working weight vector of an iteration, is capable of estimating at least polarities of the known outcome labels within a threshold tolerance of accuracy based on the quantifiable feature vectors, wherein said iterating includes computing the working weight vector in a feature space of the quantifiable features in each iteration by solving variable constraints.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A computer system, comprising: a memory storage device having executable instructions to implement: a training application programming interface configured to receive a first set of data comprising training observable events, each corresponding to a set of first quantifiable feature vectors, a set of known outcome labels, and a set of known values, wherein said known values quantify known returns associated with said known outcome labels and wherein said known outcome labels include at least a negative outcome and a positive outcome; and a model builder engine configured to provide estimated outcomes of said training observable events using a kernel function, wherein said kernel function is configured by a plurality of parameters including a match reference vector and a mismatch measurement vector, wherein said match reference vector comprises a set of values corresponding to said training observable events, and wherein said mismatch measurement vector comprises a set of values corresponding to said training observable events; calculate values of said mismatch measurement vector to provide a calculated mismatch measurement vector using said known values, wherein each value of said mismatch measurement vector is calculated from a multiple of the known value of the training observable event corresponding to said each value of said mismatch measurement vector, thereby simulating a second set of data based at least in part on said first set of data; calculate a distance between the value of said match reference vector and the value of said calculated mismatch measurement vector corresponding to each training observable event; and determine accuracy of said kernel function in providing estimated outcomes of said training observable events based at least in part on said distance, wherein said distance indicates how close each estimated outcome is to the known outcome of the training observable event corresponding to said each estimated outcome. 22. The computer system of claim 21, wherein each value of said match reference vector represents a maximum distance between the known outcome and the estimated outcome of the training observable event corresponding to said each value of said match reference vector. 23. The computer system of claim 21, wherein said model builder engine is further configured to generate a set of scores corresponding to said training observable events, wherein said scores are generated based on a dot product of a working weight vector and said first quantifiable feature vectors, wherein said working weight vector is one of the parameters of said kernel function; and rank said training observable events based on the scores corresponding to said training observable events. 24. The computer system of claim 23, wherein for each training observable event with a positive outcome, said scores corresponding to said training observable events are generated according to si=−0.25+wTxi for each training observable event with a negative outcome, said scores corresponding to said training observable events are generated according to si=0.25+wTxi wherein si is the score of the training observable event having the first quantifiable feature vector and w is said working weight vector. 25. The computer system of claim 24, wherein said calculation of each value of said mismatch measurement vector is performed by multiplying the known value of the training observable event corresponding to said each value of said mismatch measurement vector by G, wherein G is determined based on the estimated outcome of said corresponding training observable event. 26. The computer system of claim 25, wherein a value of said G is determined to adjust values of said mismatch measurement vector such that a distance between the value of said match reference vector and the value of said mismatch measurement vector corresponding to said training observable event is increased when there is a mismatch between the estimated outcome and the known outcome of said training observable event. 27. The computer system of claim 26, wherein said mismatched training observable event is one of (1) said training observable event with a positive outcome scored less than at least one other training observable event with a negative outcome and (2) said training observable event with a negative outcome scored higher than at least one other training observable event with a positive outcome. 28. The computer system of claim 27, wherein for each training observable event with a positive outcome, the corresponding value of said mismatch measurement vector (CC) is calculated according to CCri←(kri(#neg_exp−2*sn)) sp←sp−kri for each training observable event with a negative outcome, the corresponding value of said mismatch measurement vector (CCri) is calculated according to CCri←(kri(−#pos_exp=2*sp)) sn←sn+kri wherein ri represents an index indicating a rank of said training observable event in a descending order amongst said ranked training observable events, wherein #neg_exp represents a sum of all known values of said training observable events with a negative outcome and #pos_exp represents a sum of all known values of said training observable events with a positive outcome, wherein sp is initially set as a sum of all known values of said training observable events with a positive outcome and sn is initially set as 0. 29. The system of claim 28, wherein said model builder engine is further configured to apply said calculated mismatch measurement vector to determine a swapping pair, wherein said swapping pair includes at least one of said mismatch observable events. 30. The system of claim 21, wherein said kernel function is used with a support vector machine. 31. The system of claim 30, wherein said kernel function comprises either a linear function or a non-linear function. 32. The system of claim 30, wherein said model builder engine is further configured to iterate through said working weight vector of said kernel function until said kernel function is capable of estimating at least polarities of said known outcome labels within a stopping criterion based on said first quantifiable feature vectors, wherein each iteration includes computing said working weight vector in a feature space of said first quantifiable feature vectors by solving variable constraints. 33. The system of claim 32, wherein said executable instructions further comprise instructions to implement: an evaluation application programming interface configured to receive a third set of data comprising query observable events, each corresponding to a set of second quantifiable feature vectors, wherein actual outcomes of said query observable events and actual values quantifying returns associated with said actual outcomes of said query observable events are unknown to said system; and an event classification engine configured to evaluate said query observable events. 34. The system of claim 33, wherein said event classification engine is configured to estimate whether each query observable event is to yield a positive outcome or a negative outcome based on said working weight vector and said second quantifiable feature vectors. 35. The system of claim 33, wherein said event classification engine is configured to determine a ranking of each query observable event amongst said query observable events based on said working weight vector and said second set of quantifiable feature vectors, wherein said ranking represents a relative contribution of corresponding query observable event to a highest return. 36. The computer system of claim 32, wherein said parameters include a threshold value, wherein said threshold value is selected to generate a highest return based on said known values. 37. The computer system of claim 32, when said kernel function is a non-linear function, said first quantifiable feature vectors are transformed into a nonlinear space defined by said nonlinear kernel function using a low rank approximation of a corresponding kernel matrix of said kernel function. 38. The computer system of claim 37, wherein said low rank approximation is determined at least in part based on a selected plurality of centers, wherein said centers are a subset of said training observable events selected based on the known values of said training observable events. 39. The computer system of claim 38, wherein said centers are selected by a k-means clustering method.