We have invented a process and method for creating a general-purpose adaptive or static machine-learning classifier using prediction by partial matching (PPM) language modeling. This classifier can incorporate homogeneous or heterogeneous feature types; variable-size contexts; sequential or non-sequential features. Features are ordered (linearized) by information saliency; and truncation of least-informative context is used for backoff to handle previously unseen events. Labels may be endogenous (from within the group) or exogenous (outside the group) of the feature types. Classification may generate labels and their probabilities; or only labels. Classification stores may be complete or minimized where redundant states are removed producing significant space savings and performance improvements. Classifiers may be static (unchanging) or online (adaptive or updatable incrementally or in batch). PPM classifiers may be incorporated in ensembles of other PPM classifiers or different machine learning algorithms. Training and prediction algorithms are both simple and efficient; and permit multiple implementations using standard software data structures. These benefits are achieved while providing state-of-the-art prediction performance.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system for adaptive or static machine-learning classifier using prediction by partial matching (PPM) language modeling, the computer system having a readable and accessible storage medium, at least one processor and a user interface all interconnected, the computer system comprising; an instance base interconnected to a PPM classifier training module comprising; an information salience module; subinstance generation module; statistics module; and minimization modules; and a PPM classifier data storage interconnected to the PPM classifier training module and PPM classifier prediction module comprising; symbolic lookup module; and statistical lookup module. 2. The system according to claim 1, where the PPM classifier training module includes software having a set of instructions for: generating a plurality of instances each having at least one attribute, with a feature value and a class label for each of said at least one attribute; computing information saliences for each said at least one attribute; recording said saliences for a prediction element; and ordering said instances according to said saliences. 3. The system according to claim 2, where the PPM classifier training module further includes a batch training submodule having software with a set of instructions for: generating at least one sub-instances for each instance; counting said instances and said sub-instances; generating a set of escape class labels and respective counts; generating total count of said instances, said sub-instances and said respective counts of the set of escape labels; and generating complete symbolic classifier data. 4. The system according to claim 3, further comprising removing a least salient feature from each instance. 5. The system according to claim 4 further comprising determining whether an instance previously exists having a least salient feature removed therefrom. 6. The system according to claim 5 further comprising adding a supplemental instance having a class label with a count of 1, an escape count of 1 and a total count of 2. 7. The system according to claim 5 further comprising determining whether a class label has been attested. 8. The system according to claim 7, further comprising incrementing the class label and the total count for the instance with a least salient feature removed by 1. 9. The system according to claim 7, further comprising adding the class label with a count of 1, incrementing the escape count by 1 and incrementing the total count by 2. 10. The system according to claim 5, further comprising determining whether the length of instance having a least salient feature removed is greater than zero. 11. The system according to claim 2, where creating subinstances includes creating “L” length subinstance and “L−1” length subinstances. 12. The system according to claim 11, further comprising removing a least salient feature from an end of each instance. 13. The system according to claim 12, further comprising determining the frequency of class labels for L length subinstances and L−1 length subinstances. 14. The system according to claim 13, further comprising determining whether the most frequent class of L length subinstances is equivalent to the most frequent class of L−1 length subinstances. 15. The system according to claim 14, further comprising removing the L length subinstances. 16. The system according to claim 2, where the PPM classifier prediction module includes software having a set of instructions for categorizing the plurality of instances according to a predetermined salience value. 17. The system according to claim 16, further comprising selecting a prediction type from the group consisting of symbolic and statistical. 18. The system according to claim 17, further comprising selecting a statistical prediction type from the group consisting of a first method or a second method. 19. The system according to claim 17, further comprising whether each instance has the least salient feature removed. 20. The system according to claim 19, further comprising removing a lowest salience feature from each instance. 21. The system according to claim 20, further comprising determining whether each instance contains any remaining features. 22. The system according to claim 22, for each instance, attesting a class and equating a class probability with a class count divided by the total count. 23. The system according to claim 23, further comprising determining an escape probability. 24. The system according to claim 24, further comprising determining whether all classes have a positive count.