Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Classification by Discriminative Regularization 1¤ 2 3 1 1 Bin Zhang , Fei Wang , Ta-Hsin Li , Wen jun Yin , Jin Dong 1IBM China Research Lab, Beijing, China 2Department of Automation, Tsinghua University, Beijing, China 3IBM Watson Research Center, Yorktown Heights, NY, USA ¤Corresponding author, email:
[email protected] Abstract Generally achieving a good classifier needs a sufficiently ² ^ Classification is one of the most fundamental problems large amount of training data points (such that may have a better approximation of ), however, in realJ world in machine learning, which aims to separate the data J from different classes as far away as possible. A com- applications, it is usually expensive and time consuming mon way to get a good classification function is to min- to get enough labeled points. imize its empirical prediction loss or structural loss. In Even if we have enough training data points, and the con- this paper, we point out that we can also enhance the ² structed classifier may fit very well on the training set, it discriminality of those classifiers by further incorporat- may not have a good generalization ability on the testing ing the discriminative information contained in the data set as a prior into the classifier construction process. In set. This is the problem we called overfitting. such a way, we will show that the constructed classifiers Regularization is one of the most important techniques will be more powerful, and this will also be validated by to handle the above two problems. First, for the overfitting the final empirical study on several benchmark data sets.