1. A computer-implemented method, comprising: obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams: identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising: positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image.
1. A computer-implemented method, comprising: obtaining a plurality of n-grams, each of the n-grams including a unique set of one or more terms; for each of the n-grams: identifying, in a processing device, a plurality of training images for training an image classification model, the plurality of training images comprising: positive training images having relevance measures, for the n-gram, that satisfy a relevance threshold; and negative training images having relevance measures, for the n-gram, that do not satisfy the relevance threshold; selecting, in the processing device, a training image from the plurality of training images, wherein the selecting comprises semi-randomly selecting the training image subject to a selection requirement specifying that a second image be selected with a specified likelihood; classifying, in the processing device, the training image with the image classification model based on a feature vector of the training image, the feature vector comprising image feature values for the training image; and training, in the processing device, the image classification model based on the feature vector of the training image and the classification of the training image. 12. The method of claim 1 , wherein training the image classification model comprises: determining that the training image is incorrectly classified; defining the feature vector of the training image as a support vector for the image classification model; and adjusting the image classification model based on the support vector.