Perceptron Ranking Using Interval Labels with Ramp Loss for Online Ordinal Regression

Figure 1

The framework of online ordinary regression. At a round , the learner is given a question, , and is required to provide an answer to this question, . After predicting an answer, it receives the correct rank and updates its ranking rule by modifying , so that it enjoys good properties of scalability and real-time.