1. A computer implemented method of transitioning from a case-based classifier system to a rule-based classifier system, comprising: determining whether a predetermined condition is satisfied with respect to a case-based dataset stored within memory accessible by at least one processor, the case-based dataset characterized by record data identifying a plurality of records, distance data identifying a distance between pairs of each of the plurality of records, and case-wise classification data identifying class boundaries between the plurality of records based on the distance data, wherein the determining whether the predetermined condition is satisfied comprises determining whether the case-based dataset occupies an amount of memory in excess of a predetermined memory threshold; generating, for each of a plurality of rule-based classifiers, rule-based classification data identifying class boundaries between the plurality of records upon determining that the predetermined condition has been satisfied; wherein the plurality of rule-based classifiers comprise at least one of a Naïve Bayes classifier, an AdaBoost classifier, and a Rocchio classifier; computing a structural risk of the rule-based classification data for each of the plurality of rule-based classifiers with respect to the plurality of records identified within the record data; selecting rule-based classifiers having generated rule-based classification data identifying class boundaries between the plurality of records with a structural risk below a predetermined threshold; identifying selected rule-based classifiers having rule-based classification data that is within a predetermined degree of similarity with the case-wise classification data; and replacing the case-based dataset stored within the memory with rule-based classification data of at least one of the identified rule-based classifiers.
1. A computer implemented method of transitioning from a case-based classifier system to a rule-based classifier system, comprising: determining whether a predetermined condition is satisfied with respect to a case-based dataset stored within memory accessible by at least one processor, the case-based dataset characterized by record data identifying a plurality of records, distance data identifying a distance between pairs of each of the plurality of records, and case-wise classification data identifying class boundaries between the plurality of records based on the distance data, wherein the determining whether the predetermined condition is satisfied comprises determining whether the case-based dataset occupies an amount of memory in excess of a predetermined memory threshold; generating, for each of a plurality of rule-based classifiers, rule-based classification data identifying class boundaries between the plurality of records upon determining that the predetermined condition has been satisfied; wherein the plurality of rule-based classifiers comprise at least one of a Naïve Bayes classifier, an AdaBoost classifier, and a Rocchio classifier; computing a structural risk of the rule-based classification data for each of the plurality of rule-based classifiers with respect to the plurality of records identified within the record data; selecting rule-based classifiers having generated rule-based classification data identifying class boundaries between the plurality of records with a structural risk below a predetermined threshold; identifying selected rule-based classifiers having rule-based classification data that is within a predetermined degree of similarity with the case-wise classification data; and replacing the case-based dataset stored within the memory with rule-based classification data of at least one of the identified rule-based classifiers. 3. The computer implemented method of claim 1 , further comprising: obtaining input data identifying a record; and classifying the record identified by the obtained input data using rule-based classification data of at least one of the identified rule-based classifiers.