A method to improve a lithographic process for imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method including: obtaining a target feature; generating a perturbed target feature from the target feature by applying a perturbation thereto; generating a set of training examples includes the perturbed target feature and an indication as whether the perturbed target feature is deemed the same as the target feature; training a learning model with the set of training examples; classifying features in the portion of the design layout into at least two classes: being deemed the same as the target feature, and being deemed different from the target feature.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to improve a process involving imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method comprising: generating a perturbed target feature from a target feature by applying a perturbation thereto; generating a set of training examples comprising the perturbed target feature and an indication as whether the perturbed target feature is deemed the same as the target feature; training a learning model with the set of training examples; and classifying, by a hardware computer, features in the portion of the design layout into at least two classes: being deemed the same as the target feature, and being deemed different from the target feature. 2. The method of claim 1, wherein classifying the features is based on a first pixelated image of the features, the first pixelated image having a first resolution. 3. The method of claim 2, further comprising generating the first pixelated image by applying a low pass filter to the features. 4. The method of claim 1, wherein the perturbation is resizing, shifting, removing a portion of, adding a portion to the target feature, or any combination selected therefrom. 5. The method of claim 1, wherein the learning model is non-probabilistic, or wherein the learning model is a support vector machine, or wherein the learning model has a non-linear kernel. 6. The method of claim 1, wherein the learning model has a Gaussian radial basis kernel or a histogram intersection kernel. 7. The method of claim 2, wherein classifying the features comprises classifying a portion of the first pixelated image in a sliding window. 8. The method of claim 7, further comprising shifting the sliding window. 9. The method of claim 8, wherein the perturbation is shifting the target feature, and wherein the sliding window is shifted by an amount equal to or smaller than an amount of shifting in the perturbation. 10. The method of claim 2, wherein classifying the features is further based on a second pixelated image of the features, the second pixelated image having a second resolution higher than the first resolution. 11. The method of claim 1, wherein classifying the features comprises geometrically parameterizing the features and the target feature. 12. The method of claim 1, further comprising applying an adjustment of the target feature to those of the features classified as being deemed the same as the target feature. 13. A non-transitory computer program product comprising a computer readable medium having instructions recorded thereon, the instructions configured to cause a computer to: generate a perturbed target feature from a target feature by applying a perturbation thereto; generate a set of training examples comprising the perturbed target feature and an indication as whether the perturbed target feature is deemed the same as the target feature; train a learning model with the set of training examples; and classify features in the portion of the design layout into at least two classes: being deemed the same as the target feature, and being deemed different from the target feature. 14. A feature recognition method for a device manufacturing process involving processing a portion of a design layout onto a substrate, the method comprising: generating perturbed target features from a target feature by applying a perturbation thereto; generating a set of training examples comprising the perturbed target features and an indication as whether the perturbed target features are deemed the same as the target feature; training a learning model with the set of training examples; and classifying, by a computer, features in the portion of the design layout into two classes: being deemed the same as the target feature, and being deemed different from the target feature. 15. The method of claim 14, wherein classifying the features comprises classifying a portion of a pixelated image in a sliding window. 16. The method of claim 14, wherein classifying the features is based on a first pixelated image of the features, the first pixelated image having a first resolution. 17. The method of claim 16, wherein classifying the features is further based on a second pixelated image of the features, the second pixelated image having a second resolution higher than the first resolution. 18. The method of claim 14, wherein the perturbation is resizing, shifting, removing a portion of, adding a portion to the target feature, or any combination selected therefrom. 19. The method of claim 14, wherein classifying the features comprises geometrically parameterizing the features and the target feature. 20. The method of claim 14, further comprising applying an adjustment of the target feature to those of the features classified as being deemed the same as the target feature.