In one embodiment, a learning machine device initializes thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins (e.g., uniform and equidistant bins). Next, adjacent bins of the pre-defined bins having substantially similar weights may be reciprocally merged, the merging resulting in a second number of refined bins that is less than the first number. Notably, while merging, the device also learns weights of a linear decision rule associated with the one or more data features. Accordingly, a data-driven representation for a data-driven classifier may be established based on the refined bins and learned weights.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: initializing, at a learning machine device, thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins; reciprocally merging adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number; learning weights of a linear decision rule associated with the one or more data features while merging; and establishing a data-driven representation for a data-driven classifier based on the refined bins and learned weights. 2. The method as in claim 1, wherein the pre-defined bins are uniform and equidistant. 3. The method as in claim 1, further comprising: using the data-driven classifier on traffic in a computer network. 4. The method as in claim 1, further comprising: sharing the data-driven classifier with one or more other devices. 5. The method as in claim 1, wherein the data-driven classifier is a linear support vector machine (SVM). 6. The method as in claim 1, wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign. 7. The method as in claim 1, wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation. 8. The method as in claim 1, wherein the one or more features are binarized. 9. The method as in claim 1, wherein the one or more features are real-valued. 10. An apparatus, comprising: a processor configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed operable to: initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins; reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number; learn weights of a linear decision rule associated with the one or more data features while merging; and establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights. 11. The apparatus as in claim 10, wherein the pre-defined bins are uniform and equidistant. 12. The apparatus as in claim 10, wherein the process when executed is further operable to: use the data-driven classifier on traffic in a computer network. 13. The apparatus as in claim 10, wherein the process when executed is further operable to: share the data-driven classifier with one or more other devices. 14. The apparatus as in claim 10, wherein the data-driven classifier is a linear support vector machine (SVM). 15. The apparatus as in claim 10, wherein substantially similar weights comprise one or more of equal weights, similar weights, and weights having a same sign. 16. The apparatus as in claim 10, wherein the one or more features comprise a feature pair, and wherein correlation between feature values of the feature pair is used for the data representation. 17. The apparatus as in claim 10, wherein the one or more features are binarized. 18. The apparatus as in claim 10, wherein the one or more features are real-valued. 19. A tangible, non-transitory, computer-readable media having software encoded thereon, the software when executed by a processor operable to: initialize thresholds of a data representation of one or more data features, the thresholds specifying a first number of pre-defined bins; reciprocally merge adjacent bins of the pre-defined bins having substantially similar weights, the merging resulting in a second number of refined bins that is less than the first number; learn weights of a linear decision rule associated with the one or more data features while merging; and establish a data-driven representation for a data-driven classifier based on the refined bins and learned weights. 20. The computer-readable media as in claim 19, wherein the pre-defined bins are uniform and equidistant.