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A Faster Iterative Scaling Algorithm For Conditional Exponential Model

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journal contribution
posted on 01.02.2008 by Rong Jin, Rong Yan, Jian Zhang, Alexander Hauptmann
Conditional exponential model has been one of the widely used conditional models in machine learning field and improved iterative scaling (IIS) has been one of the major algorithms for finding the optimal parameters for the conditional exponential model. In this paper, we proposed a faster iterative algorithm named FIS that is able to find the optimal parameters faster than the IIS algorithm. The theoretical analysis shows that the proposed algorithm yields a tighter bound than the traditional IIS algorithm. Empirical studies on the text classification over three different datasets showed that the new iterative scaling algorithm converges substantially faster than both the IIS algorithm and the conjugate gradient algorithm (CG). Furthermore, we examine the quality of the optimal parameters found by each learning algorithm in the case of incomplete training. Experiments have shown that, when only a limited amount of computation is allowed (e.g. no convergence is achieved), the new algorithm FIS is able to obtain lower testing errors than both the IIS method and the CG method.