A method and system for data classification using machine learning comprises collecting a dataset with a data collection module, receiving the dataset at a classification module configured for machine learning, dividing the dataset into a plurality of vectors, transforming the plurality of vectors into a plurality of variables wherein each variable is assigned a label, and classifying the variables.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of machine learning for classification of data comprising: collecting a dataset with a data collection module; receiving said dataset at a classification module configured for machine learning; dividing said dataset into a plurality of vectors; transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label; and classifying said variables. 2. The method of claim 1 further comprising an offline training stage comprising: computing maximum likelihood estimates of parameters; and obtaining random variables according to a cubic-quadratic transformation. 3. The method of claim 2 wherein transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label further comprises: transforming said plurality of vectors according to said cubic-quadratic transformation from said offline training stage resulting in chi-squared random variables. 4. The method of claim 1 wherein dividing said data into a plurality of vectors further comprises: solving a program using LP solvers. 5. The method of claim 3 wherein said program is an integer linear program. 6. The method of claim 1 wherein said dataset comprises an unbalanced dataset with overlap. 7. The method of claim 6 wherein said dataset comprises data associated with one of: medical diagnosis; seismic activity; image segmentation; and drive diagnosis. 8. A system for classifying data comprising: a sensor which collects a dataset; a processor; a data bus coupled to said processor; and a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for: receiving said dataset at a classification module configured for machine learning; dividing said dataset into a plurality of vectors; transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label; and classifying said variables. 9. The system of claim 8 further comprising an offline training stage comprising: computing maximum likelihood estimates of parameters; and obtaining random variables according to a cubic-quadratic transformation. 10. The system of claim 9 wherein transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label further comprises: transforming said plurality of vectors according to said cubic-quadratic transformation from said offline training stage resulting in chi-squared random variables. 11. The system of claim 8 wherein dividing said data into a plurality of vectors further comprises: solving a program using LP solvers. 12. The system of claim 11 wherein said program is an integer linear program. 13. The system of claim 8 wherein said dataset comprises an unbalanced dataset with overlap. 14. The system of claim 13 wherein said dataset comprises data associated with one of: medical diagnosis; seismic activity; image segmentation; and drive diagnosis. 15. A medical diagnostic system comprising: a sensor which collects a dataset; a processor; a data bus coupled to said processor; and a computer-usable medium embodying computer program code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for: receiving said dataset at a classification module configured for machine learning; dividing said dataset into a plurality of vectors; transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label; and classifying said variables as indicative of the presence or absence of a medical condition. 16. The medical diagnostic system of claim 15 further comprising an offline training stage comprising: computing maximum likelihood estimates of parameters; and obtaining random variables according to a cubic-quadratic transformation. 17. The system of claim 16 wherein transforming said plurality of vectors into a plurality of variables wherein each variable is assigned a label further comprises: transforming said plurality of vectors according to said cubic-quadratic transformation from said offline training stage resulting in chi-squared random variables. 18. The system of claim 15 wherein dividing said data into a plurality of vectors further comprises: solving an integer linear program using LP solvers. 19. The system of claim 15 wherein said dataset comprises an unbalanced dataset with overlap of indicators of the presence or absence of a medical condition. 20. The system of claim 19 wherein said dataset comprises at least one indicator of the presence of absence of cancer.