1. A calibrated categorizer comprising: a multi-class categorizer configured to output class probabilities for an input object corresponding to a set of classes; a class probabilities rescaler configured to rescale class probabilities to generate rescaled class probabilities; a rescalinq model learner configured to learn calibration parameters for the class probabilities rescaler based on (i) class probabilities output by the multi-class categorizer for a calibration set of class-labeled objects, (ii) confidence measures output by the multi-class categorizer for the calibration set of class-labeled objects, and (iii) class labels of the calibration set of class-labeled objects, the class probabilities rescaler calibrated by the learned calibration parameters defining a calibrated class probabilities rescaler; a classification assignor configured to associate an input object with at least one class of the set of classes based on thresholding of resealed class probabilities for the input object, the resealed class probabilities being class probabilities output by the multi-class categorizer for the input object and resealed by the calibrated class probabilities rescaler; and a thresholds learner configured to learn thresholds for the thresholding performed by the classification assignor based on (i) resealed calibration class probabilities comprising class probabilities output by the multi-class categorizer for a thresholds calibration set of class-labeled objects and resealed by the calibrated class probabilities rescaler and (ii) class labels of the thresholds calibration set of class-labeled objects; wherein the multi-class categorizer, the class probabilities rescaler, the rescaling model learner, the classification assignor, and the thresholds learner comprise a digital data processing device.
1. A calibrated categorizer comprising: a multi-class categorizer configured to output class probabilities for an input object corresponding to a set of classes; a class probabilities rescaler configured to rescale class probabilities to generate rescaled class probabilities; a rescalinq model learner configured to learn calibration parameters for the class probabilities rescaler based on (i) class probabilities output by the multi-class categorizer for a calibration set of class-labeled objects, (ii) confidence measures output by the multi-class categorizer for the calibration set of class-labeled objects, and (iii) class labels of the calibration set of class-labeled objects, the class probabilities rescaler calibrated by the learned calibration parameters defining a calibrated class probabilities rescaler; a classification assignor configured to associate an input object with at least one class of the set of classes based on thresholding of resealed class probabilities for the input object, the resealed class probabilities being class probabilities output by the multi-class categorizer for the input object and resealed by the calibrated class probabilities rescaler; and a thresholds learner configured to learn thresholds for the thresholding performed by the classification assignor based on (i) resealed calibration class probabilities comprising class probabilities output by the multi-class categorizer for a thresholds calibration set of class-labeled objects and resealed by the calibrated class probabilities rescaler and (ii) class labels of the thresholds calibration set of class-labeled objects; wherein the multi-class categorizer, the class probabilities rescaler, the rescaling model learner, the classification assignor, and the thresholds learner comprise a digital data processing device. 8. The calibrated categorizer as set forth in claim 1 , wherein the multi-class categorizer implements a probabilistic latent semantic analysis (PLSA) classification algorithm, and the input object and the class-labeled objects are text-based documents.