Sentient Technologies (barbados) Limited

Kaivan Kamali

Babak Hodjat

Hormoz Shahrzad

Daniel Edward Fink

Roughly described, a training database contains N segments of data samples. Candidate individuals identify a testing experience level, a fitness estimate, a rule set, and a testing set TSof the data samples on which it is tested. The testing sets have fewer than all of the data segments and they are not all the same. Testing involves testing on only the individual's assigned set of data segments, updating the fitness estimates and testing experience levels, and discarding candidates through competition. If an individual reaches a predetermined maturity level of testing experience, then validating involves further testing it on samples of the testing data from a testing data segment other than those in the individual's testing set TS. Those individuals that satisfy validation criteria are considered for deployment.

Data mining technique with n-Pool evolution

Roughly described, individuals in both a training system and in a production system include a label field in their rule outputs. Positions entered by an individual are maintained in a status record for the individual, including the label output by the rule which triggered entry of that position. Rules that assert exiting or partial exiting of a position also output the label from the rule which triggered the assertion, and are effective only so far as matching positions exist or remain in the individual's status record, including a matching label. Labels present in the status record also can be referenced in conditions of a rule. During evolution, a rule's output label is subject to crossover and/or mutation just like the conditions and output assertions.

Growing community of inventors

State College, PA, United States of America

Kaivan Kamali